- Meeting abstracts
- Open Access
25th Annual Computational Neuroscience Meeting: CNS-2016
- Tatyana O. Sharpee1Email author,
- Alain Destexhe2, 3Email author,
- Mitsuo Kawato4Email author,
- Vladislav Sekulić5, 6Email author,
- Frances K. Skinner5, 6, 7, 179, 180, 182,
- Daniel K. Wójcik8Email author,
- Chaitanya Chintaluri8,
- Dorottya Cserpán9,
- Zoltán Somogyvári9,
- Jae Kyoung Kim10Email author,
- Zachary P. Kilpatrick11,
- Matthew R. Bennett12,
- Kresimir Josić11, 13,
- Irene Elices14Email author,
- David Arroyo14,
- Rafael Levi14, 15,
- Francisco B. Rodriguez14,
- Pablo Varona14Email author,
- Eunjin Hwang16, 414, 418,
- Bowon Kim16, 17, 415,
- Hio-Been Han16, 18,
- Tae Kim19,
- James T. McKenna20,
- Ritchie E. Brown20,
- Robert W. McCarley20,
- Jee Hyun Choi16, 17, 412, 414, 416, 417, 420Email author,
- James Rankin21, 421Email author,
- Pamela Osborn Popp21,
- John Rinzel21, 22,
- Alejandro Tabas23Email author,
- André Rupp24, 25,
- Emili Balaguer-Ballester23, 26,
- Matias I. Maturana27, 28,
- David B. Grayden28, 29, 249, 335, 423, 424,
- Shaun L. Cloherty30,
- Tatiana Kameneva28Email author,
- Michael R. Ibbotson27, 31,
- Hamish Meffin27, 31, 252, 253, 334Email author,
- Veronika Koren32, 33Email author,
- Timm Lochmann32, 33,
- Valentin Dragoi34,
- Klaus Obermayer32, 33,
- Maria Psarrou35Email author,
- Maria Schilstra35,
- Neil Davey35,
- Benjamin Torben-Nielsen35,
- Volker Steuber35,
- Huiwen Ju36,
- Jiao Yu37,
- Michael L. Hines38,
- Liang Chen39,
- Yuguo Yu36Email author,
- Jimin Kim40,
- Will Leahy41,
- Eli Shlizerman40, 42Email author,
- Justas Birgiolas43Email author,
- Richard C. Gerkin43,
- Sharon M. Crook2, 44,
- Atthaphon Viriyopase45, 46, 47Email author,
- Raoul-Martin Memmesheimer45, 47, 48,
- Stan Gielen45, 46,
- Yuri Dabaghian49, 50Email author,
- Justin DeVito49,
- Luca Perotti51,
- Anmo J. Kim52Email author,
- Lisa M. Fenk52,
- Cheng Cheng51,
- Gaby Maimon52,
- Chang Zhao53Email author,
- Yves Widmer54,
- Simon Sprecher54,
- Walter Senn43,
- Geir Halnes55Email author,
- Tuomo Mäki-Marttunen56, 361Email author,
- Daniel Keller57,
- Klas H. Pettersen58, 59,
- Ole A. Andreassen56, 361,
- Gaute T. Einevoll55, 60, 365,
- Yasunori Yamada61Email author,
- Moira L. Steyn-Ross62Email author,
- D. Alistair Steyn-Ross62,
- Jorge F. Mejias21Email author,
- John D. Murray63,
- Henry Kennedy64,
- Xiao-Jing Wang21, 65,
- Alexandra Kruscha66, 67Email author,
- Jan Grewe68, 69,
- Jan Benda68, 69,
- Benjamin Lindner66, 67, 169,
- Laurent Badel70Email author,
- Kazumi Ohta70,
- Yoshiko Tsuchimoto70,
- Hokto Kazama70,
- B. Kahng71Email author,
- Nicoladie D. Tam72Email author,
- Luca Pollonini73,
- George Zouridakis74,
- Jaehyun Soh75,
- DaeEun Kim75Email author,
- Minsu Yoo76Email author,
- S. E. Palmer77,
- Viviana Culmone78Email author,
- Ingo Bojak78,
- Andrea Ferrario79Email author,
- Robert Merrison-Hort79,
- Roman Borisyuk79,
- Chang Sub Kim80Email author,
- Taro Tezuka81Email author,
- Pangyu Joo82Email author,
- Young-Ah Rho83, 84Email author,
- Shawn D. Burton85, 86,
- G. Bard Ermentrout83, 86,
- Jaeseung Jeong84, 163, 164, 190, 313, 314, 316Email author,
- Nathaniel N. Urban85, 86,
- Petr Marsalek87, 88Email author,
- Hoon-Hee Kim84,
- Seok-hyun Moon89,
- Do-won Lee89,
- Sung-beom Lee89,
- Ji-yong Lee89,
- Yaroslav I. Molkov90Email author,
- Khaldoun Hamade91,
- Wondimu Teka92,
- William H. Barnett90,
- Taegyo Kim91,
- Sergey Markin91,
- Ilya A. Rybak91,
- Csaba Forro93Email author,
- Harald Dermutz93,
- László Demkó93,
- János Vörös93,
- Andrey Babichev49, 50,
- Haiping Huang94Email author,
- Sergio Verduzco-Flores95Email author,
- Filipa Dos Santos96Email author,
- Peter Andras96,
- Christoph Metzner97, 364Email author,
- Achim Schweikard98,
- Bartosz Zurowski99,
- James P. Roach100Email author,
- Leonard M. Sander101, 102,
- Michal R. Zochowski101, 102,
- Quinton M. Skilling103,
- Nicolette Ognjanovski104,
- Sara J. Aton104,
- Michal Zochowski103, 105Email author,
- Sheng-Jun Wang106, 107,
- Guang Ouyang107,
- Jing Guang108,
- Mingsha Zhang108,
- K. Y. Michael Wong109,
- Changsong Zhou107, 110, 111Email author,
- Peter A. Robinson12, 112, 113, 206,
- Paula Sanz-Leon112, 113, 206Email author,
- Peter M. Drysdale112, 113,
- Felix Fung112, 113,
- Romesh G. Abeysuriya112,
- Chris J. Rennie112, 113,
- Xuelong Zhao112, 113,
- Yoonsuck Choe114Email author,
- Huei-Fang Yang115,
- Yuanyuan Mi116, 188,
- Xiaohan Lin116,
- Si Wu116, 188Email author,
- Joscha Liedtke117, 118Email author,
- Manuel Schottdorf117, 118Email author,
- Fred Wolf117, 118,
- Yoriko Yamamura119Email author,
- Jeffery R. Wickens119,
- Timothy Rumbell120,
- Julia Ramsey121,
- Amy Reyes121,
- Danel Draguljić121,
- Patrick R. Hof122,
- Jennifer Luebke123,
- Christina M. Weaver111Email author,
- Hu He124,
- Xu Yang125Email author,
- Hailin Ma124,
- Zhiheng Xu124,
- Yuzhe Wang124,
- Kwangyeol Baek126, 127Email author,
- Laurel S. Morris126,
- Prantik Kundu128,
- Valerie Voon126,
- Everton J. Agnes129Email author,
- Tim P. Vogels129,
- William F. Podlaski130Email author,
- Martin Giese131, 344Email author,
- Pradeep Kuravi132,
- Rufin Vogels131,
- Alexander Seeholzer133Email author,
- William Podlaski134,
- Rajnish Ranjan135,
- Tim Vogels133,
- Joaquin J. Torres136,
- Fabiano Baroni137,
- Roberto Latorre14,
- Bart Gips45Email author,
- Eric Lowet45, 138,
- Mark J. Roberts45, 138,
- Peter de Weerd138,
- Ole Jensen45,
- Jan van der Eerden45,
- Abdorreza Goodarzinick139Email author,
- Mohammad D. Niry139, 140,
- Alireza Valizadeh139, 141, 142, 143,
- Aref Pariz142Email author,
- Shervin S. Parsi142,
- Julia M. Warburton144Email author,
- Lucia Marucci145,
- Francesco Tamagnini146, 147,
- Jon Brown148, 149,
- Krasimira Tsaneva-Atanasova150,
- Florence I. Kleberg151Email author,
- Jochen Triesch151, 154,
- Bahar Moezzi152Email author,
- Nicolangelo Iannella152, 153,
- Natalie Schaworonkow154,
- Lukas Plogmacher154,
- Mitchell R. Goldsworthy155,
- Brenton Hordacre155,
- Mark D. McDonnell152, 336Email author,
- Michael C. Ridding155,
- Martin Zapotocky156, 157Email author,
- Daniel Smit156, 157, 158,
- Coralie Fouquet158,
- Alain Trembleau158,
- Sakyasingha Dasgupta159, 200, 401, 402Email author,
- Isao Nishikawa161,
- Kazuyuki Aihara161, 440,
- Taro Toyoizumi160,
- Daniel T. Robb162Email author,
- Nick Mellen163,
- Natalia Toporikova164,
- Rongxiang Tang165,
- Yi-Yuan Tang166Email author,
- Guangsheng Liang166,
- Seth A. Kiser167,
- James H. HowardJr.168,
- Julia Goncharenko35Email author,
- Sergej O. Voronenko66, 169Email author,
- Tosif Ahamed170Email author,
- Greg Stephens170, 171,
- Pierre Yger172Email author,
- Baptiste Lefebvre172,
- Giulia Lia Beatrice Spampinato172,
- Elric Esposito172,
- Marcel Stimberg et Olivier Marre172,
- Hansol Choi173,
- Min-Ho Song174Email author,
- SueYeon Chung175Email author,
- Dan D. Lee176,
- Haim Sompolinsky175, 177,
- Ryan S. Phillips178, 179Email author,
- Jeffrey Smith178,
- Alexandra Pierri Chatzikalymniou179, 180Email author,
- Katie Ferguson179, 181,
- N. Alex Cayco Gajic183Email author,
- Claudia Clopath184, 410,
- R. Angus Silver183,
- Padraig Gleeson183, 271Email author,
- Boris Marin183,
- Sadra Sadeh183,
- Adrian Quintana183, 271,
- Matteo Cantarelli185,
- Salvador Dura-Bernal186, 260, 267,
- William W. Lytton186, 260, 266, 267,
- Andrew Davison187,
- Luozheng Li188,
- Wenhao Zhang188,
- Dahui Wang188, 189,
- Youngjo Song190,
- Sol Park190, 191,
- Ilhwan Choi191,
- Hee-sup Shin191,
- Hannah Choi40, 192, 193Email author,
- Anitha Pasupathy192, 193,
- Eric Shea-Brown40, 193, 425, 427,
- Dongsung Huh194Email author,
- Terrence J. Sejnowski194, 195,
- Simon M. Vogt196Email author,
- Arvind Kumar197, 198, 287, 291, 298, 302,
- Robert Schmidt196, 197,
- Stephen Van Wert199Email author,
- Steven J. Schiff199, 200,
- Richard Veale201Email author,
- Matthias Scheutz202,
- Sang Wan Lee84, 203, 204Email author,
- Júlia Gallinaro205Email author,
- Stefan Rotter205,
- Leonid L. Rubchinsky207, 208Email author,
- Chung Ching Cheung207,
- Shivakeshavan Ratnadurai-Giridharan207,
- Safura Rashid Shomali209Email author,
- Majid Nili Ahmadabadi209, 210,
- Hideaki Shimazaki211,
- S. Nader Rasuli212, 213,
- Xiaochen Zhao116,
- Malte J. Rasch116Email author,
- Jens Wilting214Email author,
- Viola Priesemann118, 214, 215, 217, 218, 224,
- Anna Levina216Email author,
- Lucas Rudelt218Email author,
- Joseph T. Lizier219, 220,
- Richard E. Spinney220,
- Mikail Rubinov221, 222,
- Michael Wibral223,
- Ji Hyun Bak225Email author,
- Jonathan Pillow226,
- Yuan Zaho228, 229,
- Il Memming Park223, 227Email author,
- Jiyoung Kang230,
- Hae-Jeong Park231Email author,
- Jaeson Jang84Email author,
- Se-Bum Paik84, 225, 232, 233,
- Woochul Choi84, 232, 233Email author,
- Changju Lee84Email author,
- Min Song84, 233Email author,
- Hyeonsu Lee84Email author,
- Youngjin Park84Email author,
- Ergin Yilmaz234Email author,
- Veli Baysal234,
- Mahmut Ozer235,
- Daniel Saska236Email author,
- Thomas Nowotny236, 237,
- Ho Ka Chan237Email author,
- Alan Diamond237,
- Christoph S. Herrmann238,
- Micah M. Murray239,
- Silvio Ionta239,
- Axel Hutt240,
- Jérémie Lefebvre241Email author,
- Philipp Weidel242Email author,
- Renato Duarte242, 243, 244,
- Abigail Morrison242, 243, 245, 246, 292, 298, 301,
- Jung H. Lee247, 433Email author,
- Ramakrishnan Iyer247, 433,
- Stefan Mihalas247, 433,
- Christof Koch247,
- Mihai A. Petrovici248Email author,
- Luziwei Leng248,
- Oliver Breitwieser247,
- David Stöckel248,
- Ilja Bytschok248,
- Roman Martel248,
- Johannes Bill248,
- Johannes Schemmel248,
- Karlheinz Meier248,
- Timothy B. Esler249Email author,
- Anthony N. Burkitt21, 28, 249,
- Robert R. Kerr250,
- Bahman Tahayori251,
- Max Nolte254Email author,
- Michael W. Reimann254,
- Eilif Muller254,
- Henry Markram254,
- Antonio Parziale255, 256Email author,
- Rosa Senatore255, 256, 257,
- Angelo Marcelli255, 256,
- K. Skiker258Email author,
- M. Maouene259,
- Samuel A. Neymotin260, 261Email author,
- Alexandra Seidenstein260, 262,
- Peter Lakatos263,
- Terence D. Sanger264, 265,
- Rosemary J. Menzies268,
- Campbell McLauchlan268,
- Sacha J. van Albada269, 292,
- David J. Kedziora268,
- Samuel Neymotin267,
- Cliff C. Kerr268Email author,
- Benjamin A. Suter270,
- Gordon M. G. Shepherd270,
- Juhyoung Ryu272Email author,
- Sang-Hun Lee272, 273, 274, 275,
- Joonwon Lee273Email author,
- Hyang Jung Lee274Email author,
- Daeseob Lim275Email author,
- Jisung Wang276,
- Heonsoo Lee276Email author,
- Nam Jung277,
- Le Anh Quang277,
- Seung Eun Maeng277,
- Tae Ho Lee277,
- Jae Woo Lee277Email author,
- Chang-hyun Park278, 279Email author,
- Sora Ahn280, 282,
- Jangsup Moon278, 279,
- Yun Seo Choi279,
- Juhee Kim280,
- Sang Beom Jun280, 281, 282,
- Seungjun Lee280, 282Email author,
- Hyang Woon Lee278, 279, 283,
- Sumin Jo282,
- Eunji Jun282,
- Suin Yu282,
- Felix Goetze284, 285Email author,
- Pik-Yin Lai284,
- Seonghyun Kim286,
- Jeehyun Kwag286Email author,
- Hyun Jae Jang286,
- Marko Filipović287, 288Email author,
- Ramon Reig289,
- Ad Aertsen287, 288,
- Gilad Silberberg290,
- Claudia Bachmann292Email author,
- Simone Buttler292,
- Heidi Jacobs293, 294, 295,
- Kim Dillen296,
- Gereon R. Fink296, 297,
- Juraj Kukolja296, 297,
- Daniel Kepple299Email author,
- Hamza Giaffar299,
- Dima Rinberg300,
- Steven Shea299,
- Alex Koulakov299,
- Jyotika Bahuguna298, 301, 302Email author,
- Tom Tetzlaff301,
- Jeanette Hellgren Kotaleski302,
- Tim Kunze303, 304Email author,
- Andre Peterson305,
- Thomas Knösche303,
- Minjung Kim306,
- Hojeong Kim306Email author,
- Ji Sung Park307,
- Ji Won Yeon307,
- Sung-Phil Kim307, 308Email author,
- Jae-Hwan Kang308,
- Chungho Lee308,
- Andreas Spiegler309Email author,
- Spase Petkoski309, 310,
- Matias J. Palva311,
- Viktor K. Jirsa309,
- Maria L. Saggio309Email author,
- Silvan F. Siep309,
- William C. Stacey312, 351, 352,
- Christophe Bernar309,
- Oh-hyeon Choung84Email author,
- Yong Jeong84,
- Yong-il Lee84, 313,
- Su Hyun Kim84, 313,
- Mir Jeong84,
- Jeungmin Lee84, 314,
- Jaehyung Kwon84, 313,
- Jerald D. Kralik84, 314Email author,
- Jaehwan Jahng84, 313Email author,
- Dong-Uk Hwang315,
- Jae-Hyung Kwon84, 316Email author,
- Sang-Min Park84, 316,
- Seongkyun Kim84,
- Hyoungkyu Kim84,
- Pyeong Soo Kim84,
- Sangsup Yoon84, 313,
- Sewoong Lim84, 313,
- Choongseok Park317Email author,
- Thomas Miller317,
- Katie Clements317,
- Sungwoo Ahn318,
- Eoon Hye Ji319,
- Fadi A. Issa317Email author,
- JeongHun Baek320Email author,
- Shigeyuki Oba320,
- Junichiro Yoshimoto321, 322,
- Kenji Doya321,
- Shin Ishii320,
- Thiago S. Mosqueiro323,
- Martin F. Strube-Bloss324,
- Brian Smith325Email author,
- Ramon Huerta323,
- Michal Hadrava325, 326, 327Email author,
- Jaroslav Hlinka326,
- Hannah Bos292Email author,
- Moritz Helias292, 328,
- Charles M. Welzig329Email author,
- Zachary J. Harper329, 330,
- Won Sup Kim331,
- In-Seob Shin331,
- Hyeon-Man Baek332,
- Seung Kee Han331Email author,
- René Richter331Email author,
- Julien Vitay331,
- Frederick Beuth331,
- Fred H. Hamker331, 332,
- Kelly Toppin333,
- Yixin Guo333Email author,
- Bruce P. Graham337,
- Penelope J. Kale338Email author,
- Leonardo L. Gollo338, 409, 431Email author,
- Merav Stern339Email author,
- L. F. Abbott340,
- Leonid A. Fedorov341, 342Email author,
- Martin A. Giese341, 342,
- Mohammad Hovaidi Ardestani343, 344Email author,
- Mohammad Javad Faraji345Email author,
- Kerstin Preuschoff346,
- Wulfram Gerstner345,
- Margriet J. van Gendt347Email author,
- Jeroen J. Briaire347,
- Randy K. Kalkman347,
- Johan H. M. Frijns347, 348,
- Won Hee Lee349Email author,
- Sophia Frangou349,
- Ben D. Fulcher350Email author,
- Patricia H. P. Tran350,
- Alex Fornito350,
- Stephen V. Gliske351,
- Eugene Lim353,
- Katherine A. Holman354,
- Christian G. Fink353, 355Email author,
- Jinseop S. Kim356, 357Email author,
- Shang Mu358,
- Kevin L. Briggman359,
- H. Sebastian Seung356, 358,
- the EyeWirers,
- Detlef Wegener360Email author,
- Lisa Bohnenkamp353, 361,
- Udo A. Ernst360,
- Anna Devor363, 364,
- Anders M. Dale362, 363, 368,
- Glenn T. Lines367,
- Andy Edwards366,
- Aslak Tveito366,
- Espen Hagen292Email author,
- Johanna Senk292,
- Markus Diesmann292, 369, 370, 376, 377, 378,
- Maximilian Schmidt371Email author,
- Rembrandt Bakker45, 370,
- Kelly Shen372,
- Gleb Bezgin373,
- Claus-Christian Hilgetag374, 375,
- Sacha Jennifer van Albada370,
- Haoqi Sun379, 380, 381, 382Email author,
- Olga Sourina379, 381,
- Guang-Bin Huang379, 381,
- Felix Klanner381, 383,
- Cornelia Denk381,
- Katharina Glomb384Email author,
- Adrián Ponce-Alvarez383,
- Matthieu Gilson383, 390Email author,
- Petra Ritter385, 386, 387, 388,
- Gustavo Deco383, 389, 390,
- Maria A. G. Witek391,
- Eric F. Clarke392,
- Mads Hansen393,
- Mikkel Wallentin394,
- Morten L. Kringelbach391, 394, 395,
- Peter Vuust391, 394,
- Guido Klingbeil396Email author,
- Erik De Schutter396, 397, 398, 399, 400,
- Weiliang Chen397Email author,
- Yunliang Zang398Email author,
- Sungho Hong403Email author,
- Akira Takashima399,
- Criseida Zamora400Email author,
- Andrew R. Gallimore400,
- Dennis Goldschmidt400Email author,
- Poramate Manoonpong401,
- Philippa J. Karoly404, 407Email author,
- Dean R. Freestone404, 405Email author,
- Daniel Soundry405,
- Levin Kuhlmann406,
- Liam Paninski405,
- Mark Cook404,
- Jaejin Lee408Email author,
- Yonatan I. Fishman409,
- Yale E. Cohen408,
- James A. Roberts410, 432Email author,
- Luca Cocchi410,
- Yann Sweeney411Email author,
- Soohyun Lee412, 413,
- Woo-Sung Jung412, 414,
- Youngsoo Kim416,
- Younginha Jung418, 419,
- Yoon-Kyu Song419,
- Frédéric Chavane422,
- Karthik Soman428,
- Vignesh Muralidharan428,
- V. Srinivasa Chakravarthy428Email author,
- Sabyasachi Shivkumar428,
- Alekhya Mandali428,
- B. Pragathi Priyadharsini428,
- Hima Mehta428,
- Catherine E. Davey424Email author,
- Braden A. W. Brinkman40, 426Email author,
- Tyler Kekona40,
- Fred Rieke426, 427,
- Michael Buice26,
- Maurizio De Pittà429, 430, 435Email author,
- Hugues Berry430, 431, 436,
- Nicolas Brunel430, 431,
- Michael Breakspear432,
- Gary Marsat437Email author,
- Jordan Drew417,
- Phillip D. Chapman417,
- Kevin C. Daly417,
- Samual P. Bradle417,
- Sat Byul Seo438Email author,
- Jianzhong Su434,
- Ege T. Kavalali439,
- Justin Blackwell434,
- LieJune Shiau440Email author,
- Laure Buhry441,
- Kanishka Basnayake442,
- Sue-Hyun Lee84, 443Email author,
- Brandon A. Levy444,
- Chris I. Baker444, 446,
- Timothée Leleu445Email author,
- Ryan T. Philips427 and
- Karishma Chhabria427
© The Author(s) 2016
- Published: 18 August 2016
A1 Functional advantages of cell-type heterogeneity in neural circuits
Tatyana O. Sharpee
A2 Mesoscopic modeling of propagating waves in visual cortex
A3 Dynamics and biomarkers of mental disorders
F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons
Vladislav Sekulić, Frances K. Skinner
F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains
Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári
F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.
Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić
O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators
Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona
O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain
Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi
O3 Modeling auditory stream segregation, build-up and bistability
James Rankin, Pamela Osborn Popp, John Rinzel
O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields
Alejandro Tabas, André Rupp, Emili Balaguer-Ballester
O5 A simple model of retinal response to multi-electrode stimulation
Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin
O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task
Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer
O7 Input-location dependent gain modulation in cerebellar nucleus neurons
Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber
O8 Analytic solution of cable energy function for cortical axons and dendrites
Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu
O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network
Jimin Kim, Will Leahy, Eli Shlizerman
O10 Is the model any good? Objective criteria for computational neuroscience model selection
Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook
O11 Cooperation and competition of gamma oscillation mechanisms
Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen
O12 A discrete structure of the brain waves
Yuri Dabaghian, Justin DeVito, Luca Perotti
O13 Direction-specific silencing of the Drosophila gaze stabilization system
Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon
O14 What does the fruit fly think about values? A model of olfactory associative learning
Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn
O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)
Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll
O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits
O17 Spatial coarse-graining the brain: origin of minicolumns
Moira L. Steyn-Ross, D. Alistair Steyn-Ross
O18 Modeling large-scale cortical networks with laminar structure
Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang
O19 Information filtering by partial synchronous spikes in a neural population
Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner
O20 Decoding context-dependent olfactory valence in Drosophila
Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama
P1 Neural network as a scale-free network: the role of a hub
P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging
Nicoladie D. Tam
P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique
Nicoladie D.Tam, Luca Pollonini, George Zouridakis
P4 Modeling jamming avoidance of weakly electric fish
Jaehyun Soh, DaeEun Kim
P5 Synergy and redundancy of retinal ganglion cells in prediction
Minsu Yoo, S. E. Palmer
P6 A neural field model with a third dimension representing cortical depth
Viviana Culmone, Ingo Bojak
P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord
Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk
P8 The recognition dynamics in the brain
Chang Sub Kim
P9 Multivariate spike train analysis using a positive definite kernel
P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia
P11 The ionic basis of heterogeneity affects stochastic synchrony
Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban
P12 Circular statistics of noise in spike trains with a periodic component
P14 Representations of directions in EEG-BCI using Gaussian readouts
Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong
P15 Action selection and reinforcement learning in basal ganglia during reaching movements
Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak
P17 Axon guidance: modeling axonal growth in T-Junction assay
Csaba Forro, Harald Dermutz, László Demkó, János Vörös
P19 Transient cell assembly networks encode persistent spatial memories
Yuri Dabaghian, Andrey Babichev
P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons
P21 Design of biologically-realistic simulations for motor control
P22 Towards understanding the functional impact of the behavioural variability of neurons
Filipa Dos Santos, Peter Andras
P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia
Christoph Metzner, Achim Schweikard, Bartosz Zurowski
P24 Memory recall and spike frequency adaptation
James P. Roach, Leonard M. Sander, Michal R. Zochowski
P25 Stability of neural networks and memory consolidation preferentially occur near criticality
Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski
P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems
Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou
P27 Neurofield: a C++ library for fast simulation of 2D neural field models
Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao
P28 Action-based grounding: Beyond encoding/decoding in neural code
Yoonsuck Choe, Huei-Fang Yang
P29 Neural computation in a dynamical system with multiple time scales
Yuanyuan Mi, Xiaohan Lin, Si Wu
P30 Maximum entropy models for 3D layouts of orientation selectivity
Joscha Liedtke, Manuel Schottdorf, Fred Wolf
P31 A behavioral assay for probing computations underlying curiosity in rodents
Yoriko Yamamura, Jeffery R. Wickens
P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models
Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver
P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm
Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang
P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis
Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon
P35 Dynamics of cooperative excitatory and inhibitory plasticity
Everton J. Agnes, Tim P. Vogels
P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons
William F. Podlaski, Tim P. Vogels
P37 Phenomenological neural model for adaptation of neurons in area IT
Martin Giese, Pradeep Kuravi, Rufin Vogels
P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment
Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels
P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations
Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona
P40 Different roles for transient and sustained activity during active visual processing
Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden
P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications
Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh
P42 High frequency neuron can facilitate propagation of signal in neural networks
Aref Pariz, Shervin S. Parsi, Alireza Valizadeh
P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus
Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova
P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP
Florence I. Kleberg, Jochen Triesch
P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex
Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch
P46 Structure and dynamics of axon network formed in primary cell culture
Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau
P47 Efficient signal processing and sampling in random networks that generate variability
Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi
P48 Modeling the effect of riluzole on bursting in respiratory neural networks
Daniel T. Robb, Nick Mellen, Natalia Toporikova
P49 Mapping relaxation training using effective connectivity analysis
Rongxiang Tang, Yi-Yuan Tang
P50 Modeling neuron oscillation of implicit sequence learning
Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang
P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus
Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber
P52 Nonlinear response of noisy neurons
Sergej O. Voronenko, Benjamin Lindner
P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion
Tosif Ahamed, Greg Stephens
P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings
Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre
P55 Sufficient sampling rates for fast hand motion tracking
Hansol Choi, Min-Ho Song
P56 Linear readout of object manifolds
SueYeon Chung, Dan D. Lee, Haim Sompolinsky
P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves
Ryan S. Phillips, Jeffrey Smith
P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus
Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner
P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning
N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver
P60 A set of curated cortical models at multiple scales on Open Source Brain
Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver
P61 A synaptic story of dynamical information encoding in neural adaptation
Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu
P62 Physical modeling of rule-observant rodent behavior
Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin
P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes
Hannah Choi, Anitha Pasupathy, Eric Shea-Brown
P65 Stability of FORCE learning on spiking and rate-based networks
Dongsung Huh, Terrence J. Sejnowski
P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments
Simon M. Vogt, Arvind Kumar, Robert Schmidt
P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation
Stephen Van Wert, Steven J. Schiff
P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo
Richard Veale, Matthias Scheutz
P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions
Sang Wan Lee
P70 Maturation of sensory networks through homeostatic structural plasticity
Júlia Gallinaro, Stefan Rotter
P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings
Paula Sanz-Leon, Peter A. Robinson
P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study
Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan
P73 Exact spike-timing distribution reveals higher-order interactions of neurons
Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli
P74 Neural mechanism of visual perceptual learning using a multi-layered neural network
Xiaochen Zhao, Malte J. Rasch
P75 Inferring collective spiking dynamics from mostly unobserved systems
Jens Wilting, Viola Priesemann
P76 How to infer distributions in the brain from subsampled observations
Anna Levina, Viola Priesemann
P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons
Lucas Rudelt, Joseph T. Lizier, Viola Priesemann
P78 A nearest-neighbours based estimator for transfer entropy between spike trains
Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann
P79 Active learning of psychometric functions with multinomial logistic models
Ji Hyun Bak, Jonathan Pillow
P81 Inferring low-dimensional network dynamics with variational latent Gaussian process
Yuan Zaho, Il Memming Park
P82 Computational investigation of energy landscapes in the resting state subcortical brain network
Jiyoung Kang, Hae-Jeong Park
P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map
Jaeson Jang, Se-Bum Paik
P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition
Woochul Choi, Se-Bum Paik
P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map
Changju Lee, Jaeson Jang, Se-Bum Paik
P86 Computational method classifying neural network activity patterns for imaging data
Min Song, Hyeonsu Lee, Se-Bum Paik
P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory
Youngjin Park, Woochul Choi, Se-Bum Paik
P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons
Ergin Yilmaz, Veli Baysal, Mahmut Ozer
P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance
Veronika Koren, Klaus Obermayer
P90 Methods for building accurate models of individual neurons
Daniel Saska, Thomas Nowotny
P91 A full size mathematical model of the early olfactory system of honeybees
Ho Ka Chan, Alan Diamond, Thomas Nowotny
P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks
Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre
P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input
Philipp Weidel, Renato Duarte, Abigail Morrison
P94 Modulation of tuning induced by abrupt reduction of SST cell activity
Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas
P95 The functional role of VIP cell activation during locomotion
Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas
P96 Stochastic inference with spiking neural networks
Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier
P97 Modeling orientation-selective electrical stimulation with retinal prostheses
Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin
P98 Ion channel noise can explain firing correlation in auditory nerves
Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell
P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit
Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram
P100 On the representation of arm reaching movements: a computational model
Antonio Parziale, Rosa Senatore, Angelo Marcelli
P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior
Rosa Senatore, Antonio Parziale, Angelo Marcelli
P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge
K. Skiker, M. Maouene
P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia
Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton
P104 Effect of network size on computational capacity
Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr
P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks
Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton
P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas
Juhyoung Ryu, Sang-Hun Lee
P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception
Joonwon Lee, Sang-Hun Lee
P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making
Hyang Jung Lee, Sang-Hun Lee
P110 A Bayesian algorithm for phoneme Perception and its neural implementation
Daeseob Lim, Sang-Hun Lee
P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol
Jisung Wang, Heonsoo Lee
P112 Self-organized criticality of neural avalanche in a neural model on complex networks
Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee
P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model
Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee
P114 Computational model to replicate seizure suppression effect by electrical stimulation
Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee
P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy
Felix Goetze, Pik-Yin Lai
P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell
Seonghyun Kim, Jeehyun Kwag
P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model
Hyun Jae Jang, Jeehyun Kwag
P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum
Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar
P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease
Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison
P120 Learning sparse representations in the olfactory bulb
Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov
P121 Functional classification of homologous basal-ganglia networks
Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski
P122 Short term memory based on multistability
Tim Kunze, Andre Peterson, Thomas Knösche
P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units
Minjung Kim, Hojeong Kim
P125 Decoding laser-induced somatosensory information from EEG
Ji Sung Park, Ji Won Yeon, Sung-Phil Kim
P126 Phase synchronization of alpha activity for EEG-based personal authentication
Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim
P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model
Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa
P130 Epileptic seizures in the unfolding of a codimension-3 singularity
Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa
P131 Incremental dimensional exploratory reasoning under multi-dimensional environment
Oh-hyeon Choung, Yong Jeong
P132 A low-cost model of eye movements and memory in personal visual cognition
Yong-il Lee, Jaeseung Jeong
P133 Complex network analysis of structural connectome of autism spectrum disorder patients
Su Hyun Kim, Mir Jeong, Jaeseung Jeong
P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip
Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong
P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making
Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong
P136 Detecting purchase decision based on hyperfrontality of the EEG
Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong
P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome
Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong
P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans
Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong
P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?
Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong
P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish
Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa
P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data
JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii
P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems
Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta
P146 Swinging networks
Michal Hadrava, Jaroslav Hlinka
P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions
Hannah Bos, Moritz Helias
P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning
Charles M. Welzig, Zachary J. Harper
P149 Multiscale complexity analysis for the segmentation of MRI images
Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han
P150 A neuro-computational model of emotional attention
René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker
P151 Multi-site delayed feedback stimulation in parkinsonian networks
Kelly Toppin, Yixin Guo
P152 Bistability in Hodgkin–Huxley-type equations
Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden
P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency
Mark D. McDonnell, Bruce P. Graham
P154 Quantifying resilience patterns in brain networks: the importance of directionality
Penelope J. Kale, Leonardo L. Gollo
P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations
Merav Stern, L. F. Abbott
P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues
Leonid A. Fedorov, Martin A. Giese
P157 Spiking model for the interaction between action recognition and action execution
Mohammad Hovaidi Ardestani, Martin Giese
P158 Surprise-modulated belief update: how to learn within changing environments?
Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner
P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation
Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns
P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks
Won Hee Lee, Sophia Frangou
P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis
Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito
P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations
Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink
P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse
Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers
P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes
Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst
P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology
Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll
P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach
Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen
P167 Local field potentials in a 4 × 4 mm2 multi-layered network model
Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann
P168 A spiking network model explains multi-scale properties of cortical dynamics
Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada
P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups
Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk
P170 Tensor decomposition reveals RSNs in simulated resting state fMRI
Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco
P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening
Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust
P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors
Guido Klingbeil, Erik De Schutter
P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS
Weiliang Chen, Erik De Schutter
P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input
Yunliang Zang, Erik De Schutter
P175 Dendritic morphology determines how dendrites are organized into functional subunits
Sungho Hong, Akira Takashima, Erik De Schutter
P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells
Criseida Zamora, Andrew R. Gallimore, Erik De Schutter
P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents
Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta
P178 Data-driven neural models part II: connectivity patterns of human seizures
Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook
P179 Data-driven neural models part I: state and parameter estimation
Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook
P180 Spectral and spatial information processing in human auditory streaming
Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen
P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain
Leonardo L. Gollo, James A. Roberts, Luca Cocchi
P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles
Yann Sweeney, Claudia Clopath
P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography
Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi
P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep
Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi
P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response
Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi
P186 Neural field model of localized orientation selective activation in V1
James Rankin, Frédéric Chavane
P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P189 A computational architecture to model the microanatomy of the striatum and its functional properties
Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching
Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy
P191 Emergence of radial orientation selectivity from synaptic plasticity
Catherine E. Davey, David B. Grayden, Anthony N. Burkitt
P192 How do hidden units shape effective connections between neurons?
Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice
P193 Characterization of neural firing in the presence of astrocyte-synapse signaling
Maurizio De Pittà, Hugues Berry, Nicolas Brunel
P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics
James A. Roberts, Leonardo L. Gollo, Michael Breakspear
P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings
Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley
P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses
Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell
P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons
LieJune Shiau, Laure Buhry, Kanishka Basnayake
P200 Visual face representations during memory retrieval compared to perception
Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker
P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics
Timothée Leleu, Kazuyuki Aihara
Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics
Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy
Tatyana O. Sharpee1
1Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, San Diego, CA, USA
Correspondence: Tatyana O. Sharpee - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):A1
Neural circuits are notorious for the complexity of their organization. Part of this complexity is related to the number of different cell types that work together to encode stimuli. I will discuss theoretical results that point to functional advantages of splitting neural populations into subtypes, both in feedforward and recurrent networks. These results outline a framework for categorizing neuronal types based on their functional properties. Such classification scheme could augment classification schemes based on molecular, anatomical, and electrophysiological properties.
1UNIC, CNRS, Gif sur Yvette, France; 2The European Institute for Theoretical Neuroscience (EITN), Paris, France
Correspondence: Alain Destexhe - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):A2
Propagating waves are large-scale phenomena widely seen in the nervous system, in both anesthetized and awake or sleeping states. Recently, the presence of propagating waves at the scale of microns–millimeters was demonstrated in the primary visual cortex (V1) of macaque monkey. Using a combination of voltage-sensitive dye (VSD) imaging in awake monkey V1 and model-based analysis, we showed that virtually every visual input is followed by a propagating wave (Muller et al., Nat Comm 2014). The wave was confined within V1, and was consistent and repeatable for a given input. Interestingly, two propagating waves always interact in a suppressive fashion, and sum sublinearly. This is in agreement with the general suppressive effect seen in other circumstances in V1 (Bair et al., J Neurosci 2003; Reynaud et al., J Neurosci 2012).
To investigate possible mechanisms for this suppression we have designed mean-field models to directly integrate the VSD experiments. Because the VSD signal is primarily caused by the summed voltage of all membranes, it represents an ideal case for mean-field models. However, usual mean-field models are based on neuronal transfer functions such as the well-known sigmoid function, or functions estimated from very simple models. Any error in the transfer function may result in wrong predictions by the corresponding mean-field model. To palliate this caveat, we have obtained semi-analytic forms of the transfer function of more realistic neuron models. We found that the same mathematical template can capture the transfer function for models such as the integrate-and-fire (IF) model, the adaptive exponential (AdEx) model, up to Hodgkin–Huxley (HH) type models, all with conductance-based inputs.
Using these transfer functions we have built “realistic” mean-field models for networks with two populations of neurons, the regular-spiking (RS) excitatory neurons, showing spike frequency adaptation, and the fast-spiking (FS) inhibitory neurons. This mean-field model can reproduce the propagating waves in V1, due to horizontal interactions, as shown previously using IF networks. This mean-field model also reproduced the suppressive interactions between propagating waves. The mechanism of suppression was based on the preferential recruitment of inhibitory cells over excitatory cells by afferent activity, which acted through the conductance-based shunting effect of the two waves onto one another. The suppression was negligible in networks with identical models for excitatory and inhibitory cells (such as IF networks). This suggests that the suppressive effect is a general phenomenon due to the higher excitability of inhibitory neurons in cortex, in line with previous models (Ozeki et al., Neuron 2009).
Work done in collaboration with Yann Zerlaut (UNIC) for modeling, Sandrine Chemla and Frederic Chavane (CNRS, Marseille) for in vivo experiments. Supported by CNRS and the European Commission (Human Brain Project).
1ATR Computational Neuroscience Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
Correspondence: Mitsuo Kawato - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):A3
Current diagnoses of mental disorders are made in a categorical way, as exemplified by DSM-5, but many difficulties have been encountered in such categorical regimes: the high percentage of comorbidities, usage of the same drug for multiple disorders, the lack of any validated animal model, and the situation where no epoch-making drug has been developed in the past 30 years. NIMH started RDoC (research domain criterion) to overcome these problems , and some successful results have been obtained, including common genetic risk loci  and common neuroanatomical changes for multiple disorders  as well as psychosis biotypes .
In contrast to the currently dominant molecular biology approach, which basically assumes one-to-one mapping between genes and disorders, I postulate the following dynamics-based view of psychiatric disorders. Our brain is a nonlinear dynamical system that can generate spontaneous spatiotemporal activities. The dynamical system is characterized by multiple stable attractors, only one of which corresponds to a healthy or typically developed state. The others are pathological states.
The most promising research approach within the above dynamical view is to combine resting-state functional magnetic resonance imaging, machine learning, big data, and sophisticated neurofeedback. Yahata et al. developed an ASD biomarker using only 16/9730 functional connections, and it did not generalize to MDD or ADHD but moderately to schizophrenia . Yamashita’s regression model of working memory ability from functional connections  generalized to schizophrenia and reproduced the severity of working-memory deficits of four psychiatric disorders (in preparation).
With the further development of machine learning algorithms and accumulation of reliable datasets, we hope to obtain a comprehensive landscape of many psychiatric and neurodevelopmental disorders. Guided by this full-spectrum structure, a tailor-made neurofeedback therapy should be optimized for each patient .
Insel T, Cuthbert B, Garvey M., et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51.
Cross-disorder group of the psychiatric genomics consortium: identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381:1371–9.
Goodkind M, et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry. 2015;72:305–15.
Clementz BA, et al. Identification of distinct psychosis biotypes using brain-based biomarkers. Am J Psychiatry. 2016;173:373–84.
Yahata N, Morimoto J, Hashimoto R, Lisi G, Shibata K, Kawakubo Y, Kuwabara H, Kuroda M, Yamada T, Megumi F, Imamizu H, Nanez JE, Takahashi H, Okamoto Y, Kasai K, Kato N, Sasaki Y, Watanabe T, Kawato M: A small number of abnormal brain connections predicts adult autism spectrum disorder. Nature Commun. 2016;7:11254. doi:10.1038/ncomms11254.
Yamashita M, Kawato M, Imamizu H. Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns. Sci Rep. 2015;5(7622). doi:10.1038/srep07622.
ATR Brain Information Communication Research Laboratory Group. DecNef Project. Available at http://www.cns.atr.jp/decnefpro/ (2016).
F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons
Vladislav Sekulić1,2, Frances K. Skinner1,2,3
1Krembil Research Institute, University Health Network, Toronto, Ontario, Canada, M5T 2S8; 2Department of Physiology, University of Toronto, Toronto, Ontario, Canada, M5S 1A8; 3 Department of Medicine (Neurology), University of Toronto, Toronto, Ontario, Canada, M5T 2S8
Correspondence: Vladislav Sekulić - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):F1
The theta rhythm (4–12 Hz) is a prominent network oscillation observed in the mammalian hippocampus and is correlated with spatial navigation and mnemonic processing. Inhibitory interneurons of the hippocampus fire action potentials at specific phases of the theta rhythm, pointing to distinct functional roles of interneurons in shaping this rhythmic activity. One hippocampal interneuron type, the oriens-lacunosum/moleculare (O-LM) cell, provides direct feedback inhibition and regulation of pyramidal cell activity in the CA1 region. O-LM cells express the hyperpolarization-activated, mixed-cation current (I h) and, in vitro, demonstrate spontaneous firing at theta that is impaired upon blockade of I h. Work using dynamic clamp has shown that in the presence of frequency-modulated artificial synaptic inputs, O-LM cells exhibit a spiking resonance at theta frequencies that is not dependent on I h . However, due to the somatic injection limitation of dynamic clamp, the study could not examine the potential contributions of putative dendritic I h or the integration of dendritically-located synaptic inputs. To overcome this, we have used a database of previously developed multi-compartment computational models of O-LM cells .
We situated our OLM cell models in an in vivo-like context by injecting Poisson-based synaptic background activities throughout their dendritic arbors. Excitatory and inhibitory synaptic weights were tuned to produce similar baseline activity prior to modulation of the inhibitory synaptic process at various frequencies (2–30 Hz). We found that models with dendritic inputs expressed enhanced resonant firing at theta frequencies compared to models with somatic inputs. We then performed detailed analyses on the outputs of the models with dendritic inputs to further elucidate these results with respect to I h distributions. The ability of the models to be recruited at the modulated input frequencies was quantified using the rotation number, or average number of spikes across all input cycles. Models with somatodendritic I h were recruited at >50 % of the input cycles for a wider range of theta frequencies (3–9 Hz) compared to models with somatic I h only (3–4 Hz). Models with somatodendritic I h also exhibited a wider range of theta frequencies for which phase-locked output (vector strength >0.75) was observed (4–12 Hz), compared to models with somatic I h (3–5 Hz). Finally, the phase of firing of models with somatodendritic I h given 8–10 Hz modulated input was delayed 180–230° relative to the time of release from inhibitory synaptic input.
O-LM cells receive phasic inhibitory inputs at theta frequencies from a subpopulation of parvalbumin-positive GABAergic interneurons in the medial septum (MS) timed to the peak of hippocampal theta, as measured in the stratum pyramidale layer . Furthermore, O-LM cells fire at the trough of hippocampal pyramidal layer theta in vivo , an approximate 180˚ phase delay from the MS inputs, corresponding to the phase delay in our models with somatodendritic I h. Our results suggest that, given dendritic synaptic inputs, O-LM cells require somatodendritic I h channel expression to be precisely recruited during the trough of hippocampal theta activity. Our strategy of leveraging model databases that encompass experimental cell type-specificity and variability allowed us to reveal critical biophysical factors that contribute to neuronal function within in vivo-like contexts.
Acknowledgements: Supported by NSERC of Canada, an Ontario Graduate Scholarship, and the SciNet HPC Consortium.
Kispersky TJ, Fernandez FR, Economo MN, White JA. Spike resonance properties in hippocampal O-LM cells are dependent on refractory dynamics. J Neurosci. 2012;32(11):3637–51.
Sekulić V, Lawrence JJ, Skinner FK. Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells. PLOS One. 2014;9(10):e106567.
Borhegyi Z, Varga V, Szilágyi, Fabo D, Freund TF. Phase segregation of medial septal GABAergic neurons during hippocampal theta activity. J Neurosci. 2004;24(39):8470–9.
Varga C, Golshani P, Soltesz I. Frequency-invariant temporal ordering of interneuronal discharges during hippocampal oscillations in awake mice. Proc Natl Acad Sci USA. 2012;109(40):E2726–34.
F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains
Daniel K. Wójcik1, Chaitanya Chintaluri1, Dorottya Cserpán2, Zoltán Somogyvári2
1Department of Neurophysiology, Nencki Institute of Experimental Biology, Warsaw, Poland; 2Department of Theory, Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Budapest, H-1121, Hungary
Correspondence: Daniel K. Wójcik - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):F2
Extracellular recordings of electric potential, with a century old history, remain a popular tool for investigations of brain activity on all scales, from single neurons, through populations, to the whole brains, in animals and humans, in vitro and in vivo . The specific information available in the recording depends on the physical settings of the system (brain + electrode). Smaller electrodes are usually more selective and are used to capture local information (spikes from single cells or LFP from populations) while larger electrodes are used for subdural recordings (on the cortex, ECoG), on the scalp (EEG) but also as depth electrodes in humans (called SEEG). The advantages of extracellular electric potential are the ease of recording and its stability. Its problem is interpretation: since electric field is long range one can observe neural activity several millimeters from its source [2–4]. As a consequence every recording reflects activity of many cells, populations and regions, depending on which level we focus. One way to overcome this problem is to reconstruct the distribution of current sources (CSD) underlying the measurement , typically done to identify activity on systems level from multiple LFP on regular grids .
We recently proposed a kernel-based method of CSD estimation from multiple LFP recordings from arbitrarily placed probes (i.e. not necessarily on a grid) which we called kernel Current Source Density method (kCSD) . In this overview we present the original proposition as well as two recent developments, skCSD (single cell kCSD) and kESI (kernel Electrophysiological Source Imaging). skCSD assumes that we know which part of the recorded signal comes from a given cell and we have access to the morphology of the cell. This could be achieved by patching a cell, driving it externally while recording the potential on a multielectrode array, injecting a dye, and reconstructing the morphology. In this case we know that the sources must be located on the cell and this information can be successfully used in estimation. In kESI we consider simultaneous recordings with subdural ECoG (strip and grid electrodes) and with depth electrodes (SEEG). Such recordings are taken on some epileptic patients prepared for surgical removal of epileptogenic zone. When MR scan of the patient head is taken and the positions of the electrodes are known as well as the brain’s shape, the idea of kCSD can be used to bound the possible distribution of sources facilitating localization of the foci.
Acknowledgements: Polish Ministry for Science and Higher Education (grant 2948/7.PR/2013/2), Hungarian Scientific Research Fund (Grant OTKA K113147), National Science Centre, Poland (Grant 2015/17/B/ST7/04123).
Buzsáki G, Anastassiou CA, Koch C. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nat Rev Neurosci. 2012;13:407–20.
Hunt MJ, Falinska M, Łęski S, Wójcik DK, Kasicki S. Differential effects produced by ketamine on oscillatory activity recorded in the rat hippocampus, dorsal striatum and nucleus accumbens. J Psychopharmacol. 2011;25:808–21.
Lindén H, Tetzlaff T, Potjans TC, Pettersen KH, Gruen S, Diesmann M, Einevoll GT. Modeling the spatial reach of the LFP. Neuron. 2011;72:859–72..
Łęski S, Lindén H, Tetzlaff T, Pettersen KH, Einevoll GT. Frequency dependence of signal power and spatial reach of the local field potential. PLoS Comput Biol. 2013;9:e1003137.
Wójcik DK. Current source density (CSD) analysis. In: Jaeger D, Jung R, editors. Encyclopedia of computational neuroscience. SpringerReference. Berlin: Springer; 2013.
Mitzdorf U. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol Rev. 1985;65:37–100.
Potworowski J, Jakuczun W, Łęski S, Wójcik DK. Kernel current source density method. Neural Comput. 2012;24:541–75.
F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks
Jae Kyoung Kim1, Zachary P. Kilpatrick2, Matthew R. Bennett3, Kresimir Josić2,4
1Department of Mathematical Sciences, KAIST, Daejoen 34141, Republic of Korea; 2Department of Mathematics, University of Houston, Houston, TX 77004, USA; 3Department of Biochemistry and Cell Biology and Institute of Biosciences and Bioengineering, Rice University, Houston, TX 77005, USA; 4Department of Biology and Biochemistry, University of Houston, Houston, TX 77004, USA
Correspondence: Jae Kyoung Kim - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):F2
In mammals, circadian (~24 h) rhythms are mainly regulated by a master circadian clock located in the suprachiasmatic nucleus (SCN) . The SCN consists of ~20,000 neurons, each of which generates own rhythms via intracellular transcriptional negative feedback loop involving PER-CRY and BMAL1-CLOCK. These individual rhythms of each neuron are synchronized through intercellular coupling via neurotransmitters including VIP . In this talk, I will discuss that the synchronized periods via coupling signal strongly depend on the mechanism of intracellular transcription repression [3–4]. Specifically, using mathematical modeling and phase response curve analysis, we find that the synchronized period of SCN stays close to the population mean of cells’ intrinsic periods (~24 h) if transcriptional repression occurs via protein sequestration. However, the synchronized period is far from the population mean when repression occurs via Hill-type regulation (e.g. phosphorylation-based repression). These results reveal the novel relationship between two major functions of the SCN-intracellular rhythm generation and intercellular synchronization of rhythms. Furthermore, this relationship provides an explanation for why the protein sequestration is commonly used in circadian clocks of multicellular organisms, which have a coupled master clock, but not in unicellular organisms .
Acknowledgements: This work was funded by the National Institutes of Health, through the joint National Science Foundation/National Institute of General Medical Sciences Mathematical Biology Program grant No. R01GM104974 (to M.R.B. and K.J.), National Science Foundation grants Nos. DMS-1311755 (to Z.P.K.) and DMS-1122094 (to K.J.), the Robert A. Welch Foundation grant No. C-1729 (to M.R.B.), National Science Foundation grant No. DMS-0931642 to the Mathematical Biosciences Institute (to J.K.K.), KAIST Research Allowance Grant G04150020 (to J.K.K) and the TJ Park Science Fellowship of POSCO TJ Park Foundation G01160001 (to J.K.K).
Dibner C, Schibler U, Albrecht U. The mammalian circadian timing system: organization and coordination of central and peripheral clocks. Annu Rev Physiol. 2010;72:517–49.
Welsh DK, Takahashi JS, Kay SA. Suprachiasmatic nucleus: cell autonomy and network properties. Annu Rev Physiol. 2010;72:551.
Kim JK, Kilpatrick ZP, Bennett MR, Josić K. Molecular mechanisms that regulate the coupled period of the mammalian circadian clock. Biophys J. 2014;106(9):2071–81.
Kim JK. Protein sequestration vs Hill-type repression in circadian clock models (in revision).
O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators
Irene Elices1, David Arroyo1, Rafael Levi1,2, Francisco B. Rodriguez1, Pablo Varona1
1Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain; 2Department of Biological Sciences, University of Southern California, CA, USA
Correspondence: Irene Elices - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O1
Found in all nervous systems, central pattern generators (CPGs) are neural circuits that produce flexible rhythmic motor patterns. Their robust and highly coordinated spatio-temporal activity is generated in the absence of rhythmic input. Several invertebrate CPGs are among the best known neural circuits, as their neurons and connections have been identified and mapped. The crustacean pyloric CPG is one of these flagship neural networks [1, 2]. Experimental and computational studies of CPGs typically examine their rhythmic output in periodic spiking-bursting regimes. Aiming to understand the fast rhythm negotiation of CPG neurons, here we present experimental and theoretical analyses of the pyloric CPG activity in situations where irregular yet coordinated rhythms are produced. In particular, we focus our study in the context of two sources of rhythm irregularity: intrinsic damage in the preparation, and irregularity induced by ethanol. The analysis of non-periodic regimes can unveil important properties of the robust dynamics controlling rhythm coordination in this system.
Adult male and female shore crabs (Carcinus maenas) were used for the experimental recordings. The isolated stomatrogastric ganglion was kept in Carcinus maenas saline. Membrane potentials were recorded intracellularly from the LP and PD cells, two mutually inhibitory neurons that form a half-center oscillator in the pyloric CPG. Extracellular electrodes allowed monitoring the overall CPG rhythm. Conductance-based models of the pyloric CPG neurons and their associated graded synapses as described in [3, 4] were also used in this dual experimental and theoretical study.
Irregularity and coordination of the CPG rhythms were analyzed using measures characterizing the cells’ instantaneous waveform, period, duty cycle, plateau, hyperpolarization and temporal structure of the spiking activity, as well as measures describing instantaneous phases among neurons in the irregular rhythms and their variability. Our results illustrate the strong robustness of the circuit to keep LP/PD phase relationships in intrinsic and induced irregularity conditions while allowing a large variety of burst waveforms, durations and hyperpolarization periods in these neurons. In spite of being electrically coupled to the pacemaker cell of the circuit, the PD neurons showed a wide flexibility to participate with larger burst durations in the CPG rhythm (and larger increase in variability), while the LP neuron was more restricted in sustaining long bursts in the conditions analyzed. The conductance-based models were used to explain the role of asymmetry in the dynamics of the neurons and synapses to shape the irregular activity observed experimentally. Taking into account the overall experimental and model analyses, we discuss the presence of preserved relationships in the non-periodic but coordinated bursting activity of the pyloric CPG, and their role in the fast rhythm negotiating properties of this circuit.
Acknowledgements: We acknowledge support from MINECO DPI2015-65833-P, TIN2014-54580-R, TIN-2012-30883 and ONRG grant N62909-14-1-N279.
Marder E, Calabrese RL. Principles of rhythmic motor pattern generation. Physiol Rev. 1996;76:687–717.
Selverston AI, Rabinovich MI, Abarbanel HDI, Elson R, Szücs A, Pinto RD, Huerta R, Varona P. Reliable circuits from irregular neurons: a dynamical approach to understanding central pattern generators. J Physiol. 2000;94:357–74.
Latorre R, Rodríguez FB, Varona P. Neural signatures: multiple coding in spiking-bursting cells. Biol Cybern. 2006;95:169–83.
Elices I, Varona P. Closed-loop control of a minimal central pattern generator network. Neurocomputing. 2015;170:55–62.
O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain
Eunjin Hwang1, Bowon Kim1,2, Hio-Been Han1,3, Tae Kim4, James T. McKenna5, Ritchie E. Brown5, Robert W. McCarley5, Jee Hyun Choi1,2
1Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, South Korea; 2Department of Neuroscience, University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejon 34113, South Korea; 3Department of Psychology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea; 4Department of Psychiatry, Kyung Hee University Hospital at Gangdong, 892, Dongnam-ro, Gangdong-gu, Seoul 05278, South Korea; 5Department of Psychiatry, Veterans Administration Boston Healthcare System and Harvard Medical School, Brockton, MA 02301, USA
Correspondence: Jee Hyun Choi - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O2
Particular behaviors are associated with different spatio-temporal patterns of cortical EEG oscillations. A recent study suggests that the cortically-projecting, parvalbumin-positive (PV+) inhibitory neurons in the basal forebrain (BF) play an important role in the state-dependent control of cortical oscillations, especially ~40 Hz gamma oscillations . However, the cortical topography of the gamma oscillations which are controlled by BF PV+ neurons and their relationship to behavior are unknown. Thus, in this study, we investigated the spatio-temporal patterns and the functional role of the cortical oscillations induced or entrained by BF PV+ neurons by combining optogenetic stimulation of BF PV+ neurons with high-density EEG [2, 3] in channelrhodopsin-2 (ChR2) transduced PV-cre mice. First, we recorded the spatio-temporal responses in the cortex with respect to the stimulation of BF PV+ neurons at various frequencies. The topographic response patterns were distinctively different depending on the stimulation frequencies, and most importantly, stimulation of BF PV+ neurons at 40 Hz (gamma band frequency) induced a preferential enhancement of gamma band oscillations in prefrontal cortex (PFC) with a statistically significant increase in intracortical connectivity within PFC. Second, optogenetic stimulation of BF PV+ neurons was applied while the mice were exposed to auditory stimuli (AS) at 40 Hz. The time delay between optogenetic stimulation and AS was tested and the phase response to the AS was characterized. We found that the phase responses to the click sound in PFC were modulated by the optogenetic stimulation of BF PV+ neurons. More specifically, the advanced activation of BF PV+ neurons by π/2 (6.25 ms) with respect to AS sharpened the phase response to AS in PFC, while the anti-phasic activation (π, 12.5 ms) blunted the phase response. Interestingly, like PFC, the primary auditory cortex (A1) also showed sharpened phase response for the π/2 advanced optogenetic BF PV+ neuron activation during AS. Considering that no direct influence of BF PV+ neurons on A1 was apparent in the response to stimulation of BF PV+ neurons alone, the sharpened phase response curve of A1 suggests a top-down influence of the PFC. This result implies that the BF PV+ neurons may participate in regulating the top-down influence that PFC exerts on primary sensory cortices during attentive behaviors, and supports the idea that the modulating activities of BF PV+ neurons might be a potential target for restoring top-down cognitive functions as well as abnormal frontal gamma oscillations associated with psychiatric disorders.
Acknowledgements: This research was supported by the Department of Veterans Affairs, the Korean National Research Council of Science & Technology (No. CRC-15-04-KIST), NIMH R01 MH039683 and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01059119). The contents of this report do not represent the views of the US Department of Veterans Affairs or the United States government.
Kim T, et al. Cortically projecting basal forebrain parvalbumin neurons regulate cortical gamma band oscillations. Proc Natl Acad Sci. 2015;112(11):3535–40.
Choi JH, et al. High resolution electroencephalography in freely moving mice. J Neurophysiol .2010;104(3):1825–34.
James Rankin1, Pamela Osborn Popp1, John Rinzel1,2
1Center for Neural Science, New York University, New York 10003, NY; 2Courant Institute of Mathematical Sciences, New York University, New York 10012, NY
Correspondence: James Rankin - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O3
Conclusions For the first time, we offer an explanation of the discrepancy in the timescales of early A1 responses and the more gradual build-up process. Recovery of A1 responses can explain resetting for stimulus pauses. Our model offers, to date, the most complete account of the early and late dynamics for auditory streaming in the triplet paradigm.
Rankin J, Sussman E, Rinzel J. Neuromechanistic model of auditory bistability. PLoS Comput Biol. 2015;11:e1004555.
Shpiro A, Moreno-Bote R, Rubin N, Rinzel J. Balance between noise and adaptation in competition models of perceptual bistability. J Comp Neurosci. 2009;27:37–54.
Micheyl C, Tian B, Carlyon R, Rauschecker J. Perceptual organization of tone sequences in the auditory cortex of awake macaques. Neuron. 2005;48:139–48.
Beauvois MW, Meddis R. Time decay of auditory stream biasing. Percept Psychophys. 1997;59:81–6.
Matias I. Maturana1,2, David B. Grayden2,3, Shaun L. Cloherty4, Tatiana Kameneva2, Michael R. Ibbotson1,5, Hamish Meffin1,5
1National Vision Research Institute, Australian College of Optometry, 3053, Australia; 2NeuroEngineering Laboratory, Dept. Electrical & Electronic Eng., University of Melbourne, 3010, Australia; 3Centre for Neural Engineering, University of Melbourne, 3010, Australia; 4Department of Physiology, Monash University, 3800, Australia; 5ARC Centre of Excellence for Integrative Brain Function, Department Optometry and Vision Sciences, University of Melbourne, 3010, Australia
Correspondence: Hamish Meffin - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O5
Retinal implants can restore vision to patients suffering photoreceptor loss by stimulating surviving retinal ganglion cells (RGCs) via an array of microelectrodes implanted within the eye . However, the acuity offered by existing devices is low, limiting the benefits to patients. Improvements may come by increasing the number of electrodes in new devices and providing patterned vision, which necessitates stimulation using multiple electrodes simultaneously. However, simultaneous stimulation poses a number of problems due to cross-talk between electrodes and uncertainty regarding the resulting activation pattern.
Here, we present a model and methods for estimating the responses of RGCs to simultaneous electrical stimulation. Whole cell in vitro patch clamp recordings were obtained from 25 RGCs with various morphological types in rat retina. The retinae were placed onto an array of 20 stimulating electrodes. Biphasic current pulses with 500 µs phase duration and 50 µs interphase gap were applied simultaneously to all electrodes at a frequency of 10 Hz, with the amplitude of current on each electrode sampled independently from a Gaussian distribution.
Furthermore, the spike-triggered ensemble showed two clusters (red and blue in Fig. 3a) corresponding to stimulation that had a net effect that was either anodic first or cathodic first. The electrical receptive fields for both anodic first and cathodic first stimulation were highly similar (Fig. 3b). They consisted of a small number (1–4) of electrodes that were close to the cell body (green dot).
The remaining 20 % of data were used to validate the model. The average model prediction root-mean-square error was 7 % over the 25 cells. The accuracy of the model indicates that the linear-nonlinear model is appropriate to describe the responses of RGCs to electrical stimulation.
Acknowledgements: This research was supported by the Australian Research Council (ARC). MI, HM, and SC acknowledge support through the Centre of Excellence for Integrative Brain Function (CE140100007), TK through ARC Discovery Early Career Researcher Award (DE120102210) and HM and TK through the ARC Discovery Projects funding scheme (DP140104533).
Hadjinicolaou AE, Meffin H, Maturana M, Cloherty SL, Ibbotson MR. Prosthetic vision: devices, patient outcomes and retinal research. Clin Exp Optom. 2015;98(5):395–410.
O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task
Veronika Koren1,2, Timm Lochmann1,2, Valentin Dragoi3, Klaus Obermayer1,2
1Institute of Software Engineering and Theoretical Computer Science, Technische Universitaet Berlin, Berlin, 10587, Germany; 2 Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universitaet zu Berlin, Berlin, 10115, Germany; 3Department of Neurobiology and Anatomy, University of Texas-Houston Medical School, Houston, TX 77030, USA
Correspondence: Veronika Koren - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O6
Linking sensory coding and behavior is a fundamental question in neuroscience. We have addressed this issue in behaving monkey visual cortex (areas V1 and V4) while animals were trained to perform a visual discrimination task in which two successive images were either rotated with respect to each other or were the same. We hypothesized that the animal’s performance in the visual discrimination task depends on the quality of stimulus coding in visual cortex. We tested this hypothesis by investigating the functional relevance of neuronal correlations in areas V1 and V4 in relation to behavioral performance. We measured two types of correlations: noise (spike count) correlations and correlations in spike timing. Surprisingly, both methods showed that correct responses are associated with significantly higher correlations in V4, but not V1, during the delay period between the two stimuli. This suggests that pair-wise interactions during the spontaneous activity preceding the arrival of the stimulus sets the stage for subsequent stimulus processing and importantly influences behavioral performance.
Experiments were conducted in 2 adult monkeys that were previously trained for the task. After 300 ms of fixation, the target stimulus, consisting of a naturalistic stimulus, is shown for 300 ms, and after a random delay period (500–1200 ms), a test stimulus is shown for 300 ms. The test can either be identical to the target stimulus (match) or rotated with respect to the target (non-match). Monkey responded by pressing a button and was rewarded for a correct response with fruit juice. Two linear arrays with 16 recording channels each were used to record population activity in areas V1 and V4. The difficulty of the task is calibrated individually to have 70 % correct responses on average. The analysis is conducted on non-match condition, comparing activity in trials with correct responses with trials where the monkey responded incorrectly. Noise correlations were assessed as pair-wise correlations of spike counts (method 1) and of spike timing (method 2). For method 1, z-scores of spike counts of binned spike trains are computed in individual trials. r_sc is computed as Pearson correlation coefficient of z-scores in all available trials, balanced across correct/incorrect condition. For the method 2, cross-correlograms were computed, from which the cross-correlograms from shuffled trials are subtracted. Resulting function was summed around zero lag and normalized with sum of autocorrelograms .
While firing rates of single units or of the population did not significantly change for correct and incorrect responses, noise correlations during the delay period were significantly higher in V4 pairs, computed with both r_sc method (p = 0.0005 in monkey 1, sign-rank test) and with r_ccg method (p = 0.0001 and p = 0.0280 in monkey 1 and 2, respectively, 50 ms integration window). This result is robust to changes in the length of the bin (method 1) and to the length of the summation window (method 2). In agreement with , we confirm the importance of spontaneous activity preceding the stimulus on performance and suggest that higher correlations in V4 might be beneficial for successful read-out and reliable transmission of the information downstream.
Bair W, Zohary E, Newsome WT. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J Neurosci. 2001; 21(5):1676–97.
Gutnisky DA, Beaman CB, Lew SE, Dragoi V. Spontaneous fluctuations in visual cortical responses influence population coding accuracy. Cereb Cortex. 2016;1–19.
Cohen MR, Maunsell JH. Attention improves performance primarily by reducing interneuronal correlations. Nat Neurosci. 2009;12(12):1594–1600.
Nienborg HR, Cohen MR, Cumming BG. Decision-related activity in sensory neurons: correlations among neurons and with behavior. Annu Rev Neurosci. 2012;35:463–83.
Maria Psarrou1, Maria Schilstra1, Neil Davey1, Benjamin Torben-Nielsen1, Volker Steuber1
Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield, AL10 9AB, UK
Correspondence: Maria Psarrou - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O7
Gain modulation is a brain-wide principle of neuronal computation that describes how neurons integrate inputs from different presynaptic sources. A gain change is a multiplicative operation that is defined as a change in the sensitivity (or slope of the response amplitude) of a neuron to one set of inputs (driving input) which results from the activity of a second set of inputs (modulatory input) [1, 2].
Different cellular and network mechanisms have been proposed to underlie gain modulation [2–4]. It is well established that input features such as synaptic noise and plasticity can contribute to multiplicative gain changes [2–4]. However, the effect of neuronal morphology on gain modulation is relatively unexplored. Neuronal inputs to the soma and dendrites are integrated in a different manner: whilst dendritic saturation can introduce a strong non-linear relationship between dendritic excitation and somatic depolarization, the relationship between somatic excitation and depolarization is more linear. The non-linear integration of dendritic inputs can enhance the multiplicative effect of shunting inhibition in the presence of noise .
Neurons in the cerebellar nuclei (CN) provide the main gateway from the cerebellum to the rest of the brain. Understanding how inhibitory inputs from cerebellar Purkinje cells interact with excitatory inputs from mossy fibres to control output from the CN is at the center of understanding cerebellar computation. In the present study, we investigated the effect of inhibitory modulatory input on CN neuronal output when the excitatory driving input was delivered at different locations in the CN neuron. We used a morphologically realistic conductance based CN neuron model  and examined the change in output gain in the presence of distributed inhibitory input under two conditions: (a) when the excitatory input was confined to one compartment (the soma or a dendritic compartment) and, (b), when the excitatory input was distributed across particular dendritic regions at different distances from the soma. For both of these conditions, our results show that the arithmetic operation performed by inhibitory synaptic input depends on the location of the excitatory synaptic input. In the presence of distal dendritic excitatory inputs, the inhibitory input has a multiplicative effect on the CN neuronal output. In contrast, excitatory inputs at the soma or proximal dendrites close to the soma undergo additive operations in the presence of inhibitory input. Moreover, the amount of the multiplicative gain change correlates with the distance of the excitatory inputs from the soma, with increasing distances from the soma resulting in increased gain changes and decreased additive shifts along the input axis. These results indicate that the location of synaptic inputs affects in a systematic way whether the input undergoes a multiplicative or additive operation.
Salinas E, Sejnowski TJ. Gain modulation in the central nervous system: where behavior, neurophysiology, and computation meet. Neuroscientist. 2001;7(5):430–40.
Silver RA. Neuronal arithmetic. Nat Rev Neurosci. 2010;11(7):474–89.
Prescott SA, De Koninck Y. Gain control of firing rate by shunting inhibition: roles of synaptic noise and dendritic saturation. Proc Natl Acad Sci USA. 2003;100(4):2076–81.
Rothman J, Cathala L, Steuber V, Silver RA. Synaptic depression enables neuronal gain control. Nature. 2009;475:1015–18.
Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D. Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. J Comput Neurosci. 2011;30(3):633–58.
Huiwen Ju1, Jiao Yu2, Michael L. Hines3, Liang Chen4 and Yuguo Yu1
1School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200438, China; 2Linyi Hospital of Traditional Chinese Medicine, 211 Jiefang Road, Lanshan, Linyi, Shandong Province, 276000, China; 3Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA; 4Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
Correspondence: Yuguo Yu - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O8
Accurate estimation of action potential (AP)-related metabolic cost is essential for understanding energetic constraints on brain connections and signaling processes. Most previous energy estimates of the AP were obtained using the Na+-counting method [1, 2], which seriously limits accurate assessment of metabolic cost of ionic currents that underlie AP generation. Moreover, the effects of axonal geometry and ion channel distribution on energy consumption related to AP propagation have not been systematically investigated.
Our analytical approach predicts an inhomogeneous distribution of metabolic cost along an axon with either uniformly or nonuniformly distributed ion channels. The results show that the Na+-counting method severely underestimates energy cost in the cable model by 20–70 %. AP propagation along axons that differ in length may require over 15 % more energy per unit of axon area than that required by a point model. However, actual energy cost can vary greatly depending on axonal branching complexity, ion channel density distributions, and AP conduction states. We also infer that the metabolic rate (i.e. energy consumption rate) of cortical axonal branches as a function of spatial volume exhibits a 3/4 power law relationship.
Acknowledgements: Dr. Yu thanks for the support from the National Natural Science Foundation of China (31271170, 31571070), Shanghai program of Professor of Special Appointment (Eastern Scholar SHH1140004).
Alle H, Roth A, Geiger JR. Energy-efficient action potentials in hippocampal mossy fibers. Science. 2009;325(5946):1405–8.
Carter BC, Bean BP. Sodium entry during action potentials of mammalian neurons: incomplete inactivation and reduced metabolic efficiency in fast-spiking neurons. Neuron. 2009;64(6):898–909.
Rall W. Cable theory for dendritic neurons. In: Methods in neuronal modeling. MIT Press; 1989. p. 9–92.
Yu Y, Hill AP, McCormick DA. Warm body temperature facilitates energy efficient cortical action potentials. PLoS Comput Biol. 2012;8(4):e1002456.
O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network
Jimin Kim1, Will Leahy2, Eli Shlizerman1,3
1Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA; 2Amazon.com Inc., Seattle, WA 98108, USA; 3Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA
Correspondence: Eli Shlizerman - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O9
Modeling neuronal systems involves incorporating the two layers: a static map of neural connections (connectome), and biophysical processes that describe neural responses and interactions. Such a model is called the ‘dynome’ of a neuronal system as it integrates a dynamical system with the static connectome. Being closer to reproducing the activity of a neuronal system, investigation of the dynome has more potential to reveal neuronal pathways of the network than the static connectome . However, since the two layers of the dynome are considered simultaneously, novel tools have to be developed for the dynome studies. Here we present a visualization methodology, called `interactome’, that allows to explore the dynome of a neuronal system interactively and in real-time, by viewing the dynamics overlaid on a graph representation of the connectome.
Our visualization and communication protocols thereby display the stimulated network in an interactive manner and permit to explore different regimes that the stimulations induce. Indeed, with the interactome we are able to recreate various experimental scenarios, such as stimulation of forward crawling (PLM/AVB neurons and/or ablation of AVB) and show that its visualization assists in identifying patterns of neurons in the stimulated network. As connectomes and dynomes of additional neuronal systems are being resolved, the interactome will enable exploring their functionality and inference to its underlying neural pathways .
Kopell NJ, Gritton HJ, Whittingon MA, Kramer MA. Beyond the connectome: the dynome. Neuron. 2014;83(6):1319–28.
Varshney LR, Chen BL, Paniagua E, Hall DH, Chkolvski DB. Structural properties of the caenorhabditis elegans neuronal network. PLoS Comput Biol. 2011;7(2):e1001066.
Kunert J, Shlizerman E, Kutz JN. Low-dimensional functionality of complex network dynamics: neurosensory integration in the Caenorhabditis elegans connectome. Phys Rev E. 2014;89(5):052805.
Bostock M, Ogievetsky V, Heer J. D3 data-driven documents. IEEE. 2011;17(12):2301–9.
Kim J, Leahy W, Shlizerman E. C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network. 2016 (in submission).
Justas Birgiolas1, Richard C. Gerkin1, Sharon M. Crook1,2
1School of Life Science, Arizona State University, Tempe, AZ 85287, USA; 2School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA
Correspondence: Justas Birgiolas - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O10
Objectively evaluating and selecting computational models of biological neurons is an ongoing challenge in the field. Models vary in morphological detail, channel mechanisms, and synaptic transmission implementations. We present the results of an automated method for evaluating computational models against property values obtained from published cell electrophysiology studies. Seven published deterministic models of olfactory bulb mitral cells were selected from ModelDB  and simulated using NEURON’s Python interface . Passive and spike properties in response to step current stimulation pulses were computed using the NeuronUnit  package and compared to their respective, experimentally obtained means of olfactory bulb mitral cell properties found in the NeuroElectro database .
In three models, the property deviations were, on average, outside the 95 % CI of the experimental means (Fig. 5 bottom), but these averages were not significant (t test p > 0.05). All other models were within the 95 % CI, while the model of Chen et al. had the lowest deviation .
Overall, the majority of these olfactory bulb mitral cell models display some properties that are not significantly different from their experimental means. However, the resting potential and input resistance properties significantly differ from the experimental values. We demonstrate that NeuronUnit provides an objective method for evaluating the fitness of computational neuroscience cell models against publicly available data.
Acknowledgements: The work of JB, RG, and SMC was supported in part by R01MH1006674 from the National Institutes of Health.
Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM. ModelDB: a database to support computational neuroscience. J Comput Neurosci. 2004;17(1):7–11.
Hines M, Davison AP, Muller E. NEURON and Python. Front Neuroinform. 2009;3:1.
Omar C, Aldrich J, Gerkin RC. Collaborative infrastructure for test-driven scientific model validation. In: Companion proceedings of the 36th international conference on software engineering. ACM; 2014. p. 524–7.
Tripathy SJ, Savitskaya J, Burton SD, Urban NN, Gerkin RC. NeuroElectro: a window to the world’s neuron electrophysiology data. Front Neuroinform. 2014;8.
Chen WR, Shen GY, Shepherd GM, Hines ML, Midtgaard J. Multiple modes of action potential initiation and propagation in mitral cell primary dendrite. J Neurophysiol. 2002;88(5):2755–64.
Atthaphon Viriyopase1,2,3, Raoul-Martin Memmesheimer1,3,4, and Stan Gielen1,2
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen (Medical Centre), The Netherlands; 2Department for Biophysics, Faculty of Science, Radboud University Nijmegen, The Netherlands; 3Department for Neuroinformatics, Faculty of Science, Radboud University Nijmegen, The Netherlands; 4Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
Correspondence: Atthaphon Viriyopase - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O11
Two major mechanisms that underlie gamma oscillations are InterNeuronal Gamma (“ING”), which is related to tonic excitation of reciprocally coupled inhibitory interneurons (I-cells), and Pyramidal InternNeuron Gamma (“PING”), which is mediated by coupled populations of excitatory pyramidal cells (E-cells) and I-cells. ING and PING are thought to serve different biological functions. Using computer simulations and analytical methods, we  therefore investigate which mechanism (ING or PING) will dominate the dynamics of a network when ING and PING interact and how the dominant mechanism may switch.
Our study suggests experimental approaches to decide whether oscillatory activity in networks of interacting excitatory and inhibitory neurons is dominated by ING or PING oscillations and whether the participating interneurons belong to class I or II. Consider as an example networks with type-I interneurons where the external drive to the E-cells, I0,E, is kept constant while the external drive to the I-cells, I0,I, is varied. For both ING and PING dominated oscillations the frequency of the rhythm increases when I0,I increases (cf. Fig. 6D). Observing such an increase does therefore not allow to determine the underlying mechanism. However, the absolute value of the first derivative of the frequency with respect to I0,I allows a distinction, as it is much smaller for PING than for ING (cf. Fig. 6D). In networks with type-II interneurons, the non-monotonic dependence near the ING-PING transition may be a characteristic hallmark to detect the oscillation character (and the interneuron type): Decrease (increase) of the frequency when increasing I0,E indicates ING (PING), cf. Fig. 6E. These theoretical predictions are in line with experimental evidence .
Viriyopase A, Memmesheimer RM, Gielen S. Cooperation and competition of gamma oscillation mechanisms. J Neurophysiol. 2016.
Craig MT, McBain CJ. Fast gamma oscillations are generated intrinsically in CA1 without the involvement of fast-spiking basket cells. J Neurosci. 2015;35(8):3616–24.
Yuri Dabaghian1,2, Justin DeVito1, Luca Perotti3
1Department of Neurology Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; 2Department of Computational and Applied Mathematics, Rice University, Houston, TX, 77005, USA; 3Physics Department, Texas Southern University, 3100 Cleburne St, Houston, TX 77004, USA
Correspondence: Yuri Dabaghian - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O12
A physiological interpretation of the biological rhythms, e.g., of the local field potentials (LFP) depends on the mathematical and computational approaches used for its analysis. Most existing mathematical methods of the LFP studies are based on braking the signal into a combination of simpler components, e.g., into sinusoidal harmonics of Fourier analysis or into wavelets of the Wavelet Analysis. However, a common feature of all these methods is that their prime components are presumed from the onset, and the goal of the subsequent analysis reduces to identifying the combination that best reproduces the original signal.
We propose a fundamentally new method, based on a number of deep theorems of complex function theory, in which the prime components of the signal are not presumed a priori, but discovered empirically . Moreover, the new method is more flexible and more sensitive to the signal’s structure than the standard Fourier method.
Applying this method reveals a fundamentally new structure in the hippocampal LFP signals in rats in mice. In particular, our results suggest that the LFP oscillations consist of a superposition of a small, discrete set of frequency modulated oscillatory processes, which we call “oscillons”. Since these structures are discovered empirically, we hypothesize that they may capture the signal’s actual physical structure, i.e., the pattern of synchronous activity in neuronal ensembles. Proving this hypothesis will help enormously to advance a principal, theoretical understanding of the neuronal synchronization mechanisms. We anticipate that it will reveal new information about the structure of the LFP and other biological oscillations, which should provide insights into the underlying physiological phenomena and the organization of brains states that are currently poorly understood, e.g., sleep and epilepsy.
Acknowledgements: The work was supported by the NSF 1422438 grant and by the Houston Bioinformatics Endowment Fund.
Perotti L, DeVito J, Bessis D, Dabaghian Y, Dabaghian Y, Brandt VL, Frank LM. Discrete spectra of brain rhythms (in submisison).
Anmo J. Kim1,†, Lisa M. Fenk1,†, Cheng Lyu1, Gaby Maimon1
1Laboratory of Integrative Brain Function, The Rockefeller University, New York, NY 10065, USA
Correspondence: Anmo J. Kim - email@example.com
† Authors contributed equally
BMC Neuroscience 2016, 17(Suppl 1):O13
Many animals, including insects and humans, stabilize the visual image projected onto their retina by following a rotating landscape with their head or eyes. This stabilization reflex, also called the optomotor response, can pose a problem, however, when the animal intends to change its gaze. To resolve this paradox, von Holst and Mittelstaedt proposed that a copy of the motor command, or efference copy, could be routed into the visual system to transiently silence this stabilization reflex when an animal changes its gaze . Consistent with this idea, we recently demonstrated that a single identified neuron associated with the optomotor response receives silencing motor-related inputs during rapid flight turns, or saccades, in tethered, flying Drosophila .
von Holst E, Mittelstaedt H. The principle of reafference. Naturwissenschaften.1950;37:464–76.
Kim AJ, Fitzgerald JK, Maimon G. Cellular evidence for efference copy in Drosophila visuomotor processing. Nat Neurosci. 2015;18:1247–55.
Schilstra C, van Hateren JH. Stabilizing gaze in flying blowflies. Nature. 1998;395:654.
Chang Zhao1, Yves Widmer2, Simon Sprecher2, Walter Senn1
1Department of Physiology, University of Bern, Bern, 3012, Switzerland; 2Department of Biology, University of Fribourg, Fribourg, 1700, Switzerland
Correspondence: Chang Zhao - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O14
Associative learning in the fruit fly olfactory system has been studied from the molecular to the behavior level [1, 2]. Fruit flies are able to associate conditional stimuli such as odor with unconditional aversive stimuli such as electrical shocks, or appetitive stimuli such as sugar or water. The mushroom body in the fruit fly brain is considered to be crucial for olfactory learning [1, 2]. The behavioral experiments show that the learning can not be explained simply by an additive Hebbian (i.e. correlation-based) learning rule. Instead, it depends on the timing between the conditional and unconditional stimulus presentation. Yarali and colleagues suggested a dynamic model on the molecular level to explain event timing in associative learning . Here, we present new experiments together with a simple phenomenological model for learning that shows that associative olfactory learning in the fruit fly represents value learning that is incompatible with Hebbian learning.
In our model, the information of the conditional odor stimulus is conveyed by Kenyon cells from the projection neurons to the mushroom output neurons; the information of the unconditional shock stimulus is represented by dopaminergic neurons to the mushroom output neurons through direct or indirect pathways. The mushroom body output neurons encode the internal value (v) of the odor (o) by synaptic weights (w) that conveys the odor information, v = w∙o. The synaptic strength is updated according to the value learning rule, Δw = η(s − v)õ, where s represents the (internal) strength of the shock stimulus, õ represents the synaptic odor trace, and η is the learning rate. The value associated with the odor determines the probability of escaping from that odor. This simple model reproduces the behavioral data and shows that olfactory conditioning in the fruit fly is in fact value learning. In contrast to the prediction of Hebbian learning, the escape probability for repeated odor-shock pairings is much lower than the escape probability for a single pairing with a correspondingly stronger shock.
Aso Y, Sitaraman D, Ichinose T, Kaun KR, Vogt K, Belliart-Gurin G, Plaais PY, Robie AA, Yamagata N, Schnaitmann C, Rowell WJ, Johnston RM, Ngo TB, Chen N, Korff W, Nitabach MN, Heberlein U, Preat T, Branson KM, Tanimoto H, Rubin GM: Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila. ELife. 2014;3:e04580.
Heisenberg M. Mushroom body memoir: from maps to models. Nat Rev Neurosci. 2003;4:266–75.
Yarali A, Nehrkorn J, Tanimoto H, Herz AVM. Event timing in associative learning: from biochemical reaction dynamics to behavioural observations. PLoS One. 2012;7(3):e32885.
Geir Halnes1, Tuomo Mäki-Marttunen2, Daniel Keller3, Klas H. Pettersen4,5,Ole A. Andreassen2, Gaute T. Einevoll1,6
1Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway; 2NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; 3The Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; 4Letten Centre and Glialab, Department of Molecular Medicine, Instotute of Basic Medical Sciences, University of Oslo, Oslo, Norway; 5Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway; 6Department of Physics, University of Oslo, Oslo, Norway
Correspondence: Geir Halnes - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O15
The local field potential (LFP) in the extracellular space (ECS) of the brain, is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the LFP and its underlying neurophysiological processes are commonly used in the interpretation such measurements. Standard methods, such as volume conductor theory , assume that ionic diffusion in the ECS has negligible impact on the LFP. This assumption could be challenged during endured periods of intense neural signalling, under which local ion concentrations in the ECS can change by several millimolars. Such concentration changes are indeed often accompanied by shifts in the ECS potential, which may be partially evoked by diffusive currents . However, it is hitherto unclear whether putative diffusion-generated potential shifts are too slow to be picked up in LFP recordings, which typically use electrode systems with cut-off frequencies at ~0.1 Hz.
To explore possible effects of diffusion on the LFP, we developed a hybrid simulation framework: (1) The NEURON simulator was used to compute the ionic output currents from a small population of cortical layer-5 pyramidal neurons . The neural model was tuned so that simulations over ~100 s of biological time led to shifts in ECS concentrations by a few millimolars, similar to what has been seen in experiments . (2) In parallel, a novel electrodiffusive simulation framework  was used to compute the resulting dynamics of the potential and ion concentrations in the ECS, accounting for the effect of electrical migration as well as diffusion. To explore the relative role of diffusion, we compared simulations where ECS diffusion was absent with simulations where ECS diffusion was included.
Holt G, Koch C. Electrical interactions via the extracellular potential near cell bodies. J Comput Neurosci. 1999;6:169–84.
Dietzel I, Heinemann U, Lux H. Relations between slow extracellular potential changes, glial potassium buffering, and electrolyte and cellular volume changes during neuronal hyperactivity in cat. Glia. 1989;2:25–44.
Hay E, Hill S, Schürmann F, Markram H, Segev I. Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol. 2011;7(7):e1002107.
Halnes G, Østby I, Pettersen KH, Omholt SW, Einevoll GT: Electrodiffusive model for astrocytic and neuronal ion concentration dynamics. PLoS Comput Biol. 2013;9(12):e1003386.
O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits
1IBM Research - Tokyo, Japan
Correspondence: Yasunori Yamada - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O16
Acknowledgements: This research was partially supported by the Japan Science and Technology Agency (JST) under the Strategic Promotion of Innovative Research and Development Program.
Stam CJ. Modern network science of neurological disorders. Nat Rev Neurosci. 2014;15(10):683–695.
Brown JA, Terashima KH, Burggren AC, Ercoli LM, Miller KJ, Small GW, Bookheimer SY. Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci USA. 2011;108(51):20760–5.
Brown JA, Rudie JD, Bandrowski A, van Horn JD, Bookheimer SY. The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Front Neuroinform. 2012;6(28).
Ikegaya Y, Sasaki T, Ishikawa D, Honma N, Tao K, Takahashi N, Minamisawa G, Ujita S, Matsuki N. Interpyramid spike transmission stabilizes the sparseness of recurrent network activity. Cereb Cortex. 2013;23(2):293–304.
Moira L. Steyn-Ross1, D. Alistair Steyn-Ross1
1School of Engineering, University of Waikato, Hamilton 3240, New Zealand
Correspondence: Moira L. Steyn-Ross - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O17
The seminal experiments of Mountcastle  over 60 years ago established the existence of cortical minicolumns: vertical column-like arrays of approximately 80–120 neurons aligned perpendicular to the pial surface, penetrating all six cortical layers. Minicolumns have been proposed as the fundamental unit for cortical organisation. Minicolumn formation is thought to rely on gene expression and thalamic activity, but exactly why neurons cluster into columns of diameter 30–50 μm containing approximately 100 neurons is not known.
In this presentation we describe a mechanism for the formation of minicolumns via gap-junction diffusion-mediated coupling in a network of spiking neurons. We use our recently developed method of cortical “reblocking” (spatial coarse-graining)  to derive neuronal dynamics equations at different spatial scales. We are able to show that for sufficiently strong gap-junction coupling, there exists a minimum block size over which neural activity is expected to be coherent. This coherence region has cross-sectional area of order (40–60 μm)2, consistent with the areal extent of a minicolumn. Our scheme regrids a 2D continuum of spiking neurons using a spatial rescaling theory, established in the 1980s, that systematically eliminates high-wave-number modes . The rescaled neural equations describe the bulk dynamics of a larger block of neurons giving “true” (rather than mean-field) population activity, encapsulating the inherent dynamics of a continuum of spiking neurons stimulated by incoming signals from neighbors, and buffeted by ion-channel and synaptic noise.
Our method relies on a perturbative expansion. In order for this coarse-graining expansion to converge, we require not only a sufficiently strong level of inhibitory gap-junction coupling, but also a sufficiently large blocking ratio B. The latter condition establishes a lower bound for the smallest “cortical block”: the smallest group of neurons that can respond to input as a collective and cooperative unit. We find that this minimum block-size ratio lies between 4 and 6. In order to relate this 2D geometric result to the 3D extent of a 3-mm-thick layered cortex, we project the cortex onto a horizontal surface and count the number of neurons contained within each l × l grid micro-cell. Setting l ≈ 10 μm and assuming an average of one interneuron per grid cell, a blocking ratio at the mid-value B = 5 implies that the side-length of a coherent “macro-cell” will be L = Bl = 50 μm containing ~25 inhibitory plus 100 excitatory neurons (assuming an i to e abundance ratio of 1:4) in cross-sectional area L 2. Thus the minicolumn volume will contain roughly 125 neurons. We argue that this is the smallest diffusively-coupled population size that can support cooperative dynamics, providing a natural mechanism defining the functional extent of a minicolumn.
We propose that minicolumns might form in the developing brain as follows: Inhibitory neurons migrate horizontally from the ganglionic eminence to form a dense gap-junction coupled substrate that permeates all layers of the cortex . Progenitor excitatory cells ascend vertically from the ventricular zone, migrating through the inhibitory substrate of the cortical plate. Thalamic input provides low-level stimulus to activate spiking activity throughout the network. Inhibitory diffusive coupling allows a “coarse graining” such that neurons within a particular areal extent respond collectively to the same input. The minimum block size prescribed by the coarse graining imposes constraints on minicolumn geometry, leading to the spontaneous emergence of cylindrical columns of coherent activity, each column centered on an ascending chain of excitatory neurons and separated from neighboring chains by an annular surround of inhibition. This smallest aggregate is preferentially activated during early brain development, and activity-based plasticity then leads to the formation of tangible structural columns.
Mountcastle VB. Modality and topographic properties of single neurons of cat’s somatic sensory cortex. J Neurophysiol. 1957;20(4):408–34.
Steyn-Ross ML, Steyn-Ross DA. From individual spiking neurons to population behavior: Systematic elimination of short-wavelength spatial modes. Phys Rev E. 2016;93(2):022402.
Steyn-Ross ML, Gardiner CW. Adiabatic elimination in stochastic systems III. Phys Rev A. 1984;29(5):2834–44.
Jones EG. Microcolumns in the cerebral cortex. Proc Natl Acad Sci USA. 2000;97(10):5019–21.
Jorge F. Mejias1, John D. Murray2, Henry Kennedy3, and Xiao-Jing Wang1,4
1Center for Neural Science, New York University, New York, NY, 10003, USA; 2Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA; 3INSERM U846, Stem Cell and Brain Research Institute, Bron Cedex, France; 4NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai, China
Correspondence: Jorge F. Mejias - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O18
Visual cortical areas in the macaque are organized according to an anatomical hierarchy, which is defined by specific patterns of anatomical projections in the feedforward and feedback directions [1, 2]. Recent macaque studies also suggest that signals ascending through the visual hierarchy are associated with gamma rhythms, and top-down signals with alpha/low beta rhythms [3–5]. It is not clear, however, how oscillations presumably originating at local populations can give rise to such frequency-specific large-scale interactions in a mechanistic way, or the role that anatomical projections patterns might have in this.
To address this question, we build a large-scale cortical network model with laminar structure, grounding our model on a recently obtained anatomical connectivity matrix with weighted directed inter-areal projections and information about their laminar origin. The model involves several spatial scales—local or intra-laminar microcircuit, inter-laminar circuits, inter-areal interactions and large-scale cortical network—and a wide range of temporal scales—from slow alpha oscillations to gamma rhythms. At any given level, the model is constrained anatomically and then tested against electrophysiological observations, which provides useful information on the mechanisms modulating the oscillatory activity at different scales. As we ascend through the local to the inter-laminar and inter-areal levels, the model allows us to explore the sensory-driven enhancement of gamma rhythms, the inter-laminar phase-amplitude coupling, the relationship between alpha waves and local inhibition, and the frequency-specific inter-areal interactions in the feedforward and feedback directions [3, 4], revealing a possible link with the predictive coding framework.
When we embed our modeling framework into the anatomical connectivity matrix of 30 areas (which includes novel areas not present in previous studies [2, 6]), the model gives insight into the mechanisms of large-scale communication across the cortex, accounts for an anatomical and functional segregation of FF and FB interactions, and predicts the emergence of functional hierarchies, which recent studies have found in macaque  and human . Interestingly, the functional hierarchies observed experimentally are highly dynamic, with areas moving across the hierarchy depending on the behavioral context . In this regard, our model provides a strong prediction: we propose that these hierarchical jumps are triggered by laminar-specific modulations of input into cortical areas, suggesting a strong link between hierarchy dynamics and context-dependent computations driven by specific inputs.
Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex. 1991;1(1):1–47.
Markov NT, Vezoli J, Chameau P, Falchier A, Quilodran R, Huissoud C, Lamy C, Misery P, Giroud P, Ullman S, et al. Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. J Comp Neurol. 2014;522:225–259.
van Kerkoerle T, Self MW, Dagnino B, Gariel-Mathis MA, Poort J, van der Togt C, Roelfsema PR. Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proc Natl Acad Sci USA. 2014;111;14332–41.
Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, De Weerd P, Kennedy H, Fries P. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron. 2015;85:390–401.
Michalareas G, Vezoli J, van Pelt S, Schoffelen JM, Kennedy H, Fries. Alpha–beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron. 2016;89:384–97.
Chaudhuri R, Knoblauch K, Gariel MA, Kennedy H, Wang XJ. A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron. 2015;88:419–31.
Alexandra Kruscha1,2, Jan Grewe3,4, Jan Benda3,4 and Benjamin Lindner1,2
1Bernstein Center for Computational Neuroscience, Berlin, 10115, Germany; 2Institute for Physics, Humboldt-Universität zu Berlin, Berlin, 12489, Germany; 3Institue for Neurobiology, Eberhardt Karls Universität Tübingen, Germany; 4Bernstein Center for Computational Neuroscience, Munich, Germany
Correspondence: Alexandra Kruscha - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):O19
Synchronous firing of neurons is a prominent feature in many brain areas. Here, we are interested in the information transmission by the synchronous spiking output of a noisy neuronal population, which receives a common time-dependent sensory stimulus. Earlier experimental  and theoretical  work revealed that synchronous spikes encode preferentially fast (high-frequency) components of the stimulus, i.e. synchrony can act as an information filter. In these studies a rather strict measure of synchrony was used: the entire population has to fire within a short time window. Here, we generalize the definition of the synchronous output, for which only a certain fraction γ of the population needs to be active simultaneously—a setup that seems to be of more biological relevance. We characterize the information transfer in dependence of this fraction and the population size, by the spectral coherence function between the stimulus and the partial synchronous output. We present two different analytical approaches to derive this frequency-resolved measure (one that is more suited for small population sizes, while the second one is applicable to larger populations). We show that there is a critical synchrony fraction, namely the probability at which a single neuron spikes within the predefined time window, which maximizes the information transmission of the synchronous output. At this value, the partial synchronous output acts as a low-pass filter, whereas deviations from this critical fraction lead to a more and more pronounced band-pass filtering effect. We confirm our analytical findings by numerical simulations for the leaky integrate-and-fire neuron. We also show that these findings are supported by experimental recordungs of P-Units electroreceptors of weakly electric fish, where the filtering effect of the synchronous output occurs in real neurons as well.
Acknowledgement: This work was supported by Bundesministerium für Bildung und Forschung Grant 01GQ1001A and DFG Grant 609788-L1 1046/2-1.
Middleton JW, Longtin A, Benda J, Maler L. Postsynaptic receptive field size and spike threshold determine encoding of high-frequency information via sensitivity to synchronous presynaptic activity. J Neurophysiol. 2009;101:1160–70.
Sharafi N, Benda J, Lindner B. Information filtering by synchronous spikes in a neural population. J Comp Neurosc. 2013;34:285–301.
Laurent Badel1, Kazumi Ohta1, Yoshiko Tsuchimoto1, Hokto Kazama1
1RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, 351-0198, Japan
Correspondence: Laurent Badel - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):O20
Many animals rely on olfactory cues to make perceptual decisions and navigate the environment. In the brain, odorant molecules are sensed by olfactory receptor neurons (ORNs), which convey olfactory information to the central brain in the form of sequences of action potentials. In many organisms, axons of ORNs expressing the same olfactory receptor converge to one or a few glomeruli in the first central region (the antennal lobe in insects and the olfactory bulb in fish and mammals) where they make contact with their postsynaptic targets. Therefore, each glomerulus can be considered as a processing unit that relays information from a specific type of receptor. Because different odorants recruit different sets of glomeruli, and most glomeruli respond to a wide array of odors, olfactory information at this stage of processing is contained in spatiotemporal patterns of glomerular activity. How these patterns are decoded by the brain to guide odor-evoked behavior, however, remains largely unknown.
In Drosophila, attraction and aversion to specific odors have been linked to the activation of one or a few glomeruli (reviewed in ) in the antennal lobe (AL). These observations suggest a “labeled-line” coding strategy, in which individual glomeruli convey signals of specific ethological relevance, and their activation triggers the execution of hard-wired behavioral programs. However, because these studies used few odorants, and a small fraction of glomeruli were tested, it is unclear how the results generalize to broader odor sets, and whether similar conclusions hold for each of the ~50 glomeruli of the fly AL. Moreover, how compound signals from multiple glomeruli are integrated is poorly understood.
Here, we combine optical imaging, behavioral and statistical techniques to address these questions systematically. Using two-photon imaging, we monitor Ca2+ activity in the AL in response to 84 odors. We next screen behavioral responses to the same odorants. Comparing these data allows us to formulate a decoding model describing how olfactory behavior is determined by glomerular activity patterns in a quantitative manner. We find that a weighted sum of normalized glomerular responses recapitulates the observed behavior and predicts responses to novel odors, suggesting that odor valence is not determined solely by the activity a few privileged glomeruli. This conclusion is supported by genetic silencing and optogenetic activation of individual ORN types, which are found to evoke modest biases in behavior in agreement with model predictions. Finally, we test the model prediction that the relative valence of a pair of odors depends on the identity of other odors presented in the same experiment. We find that the relative valence indeed changes, and may even switch, suggesting that perceptual decisions can be modulated by the olfactory context. Surprisingly, our model correctly captured both the direction and the magnitude of the observed changes. These results indicate that the valence of olfactory stimuli is decoded from AL activity by pooling contributions over a large number of glomeruli, and highlight the ability of the olfactory system to adapt to the statistics of its environment, similarly to the visual and auditory systems.
Li Q, Liberles SD. Aversion and attraction through olfaction. Curr Biol. 2015;25(3):R120–9.
1Department of Physics and Astronomy, Seoul National University, 08826, Korea
Correspondence: B. Kahng - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):P1
Recently, increasing attention has been drawn to human neuroscience in network science communities. This is because recent fMRI and anatomical experiments have revealed that neural networks of normal human brain are scale-free networks. Thus, accumulated knowledges in a broad range of network sciences can be naturally applied to neural networks to understand functions and properties of normal and disordered human brain networks. Particularly, the degree exponent value of the human neural network constructed from the fMRI data turned out to be approximately two. This value has particularly important meaning in scale-free networks, because the number of connections to neighbors of a hub becomes largest and thus functional role of the hub becomes extremely important. In this talk, we present the role of the hub in pattern recognition and dynamical problems in association with neuroscience.
Nicoladie D. Tam1
1Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA
Correspondence: Nicoladie D. Tam - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):P2
This study focuses on the relationship between the emotional response, decision and the hemodynamic responses in the prefrontal cortex. This is based on the computational emotional model that hypothesizes the emotional response is proportional to the discrepancy between the expectancy and the actuality. Previous studies had shown that emotional responses are related to decisions [1, 2]. Specifically, the emotional responses of happy , sad , angry , jealous  emotions are proportional to the discrepancy between what one wants and what one gets [1, 3–7].
Methods Human subjects are asked to perform the classical behavioral economic experiment called Ultimatum Game (UG) . This experimental paradigm elicits the interrelationship between decision and emotion in human subjects [3–6]. The hemodynamic responses of the prefrontal cortex were recorded while the subjects performed the UG experiment.
Results The results showed that the hemodynamic response, which corresponds to the neural activation and deactivation based on the metabolic activities of the neural tissues, are proportional to the emotional intensity and the discrepancy between the expectancy and the actuality. This validates the hypothesis of the proposed emotional theory [9–11] that the intensity of emotion is proportional to the disparity between the expected and the actual outcomes. These responses are also related to the fairness perception , with respect to the survival functions [9, 10] similar to the responses established for happy  emotion, and for fairness  experimentally. This is consistent with the computational relationship between decision and fairness .
Tam ND. Quantification of happy emotion: dependence on decisions. Psychol Behav Sci. 2014;3(2):68–74.
Tam ND. Rational decision-making process choosing fairness over monetary gain as decision criteria. Psychol Behav Sci. 2014;3(6–1):16–23.
Tam ND. Quantification of happy emotion: Proportionality relationship to gain/loss. Psychol Behav Sci. 2014;3(2):60–7.
Tam ND: Quantitative assessment of sad emotion. Psychol Behav Sci 2015, 4(2):36-43.
Tam DN. Computation in emotional processing: quantitative confirmation of proportionality hypothesis for angry unhappy emotional intensity to perceived loss. Cogn Comput. 2011;3(2):394–415.
Tam ND, Smith KM. Cognitive computation of jealous emotion. Psychol Behav Sci. 2014;3(6–1):1–7.
Tam ND. Quantification of fairness perception by including other-regarding concerns using a relativistic fairness-equity model. Adv Soc Sci Research J. 2014;1(4):159–69.
von Neumann J, Morgenstern O, Rubinstein A. Theory of games and economic behavior. Princeton: Princeton University Press; 1953.
Tam D. EMOTION-I model: A biologically-based theoretical framework for deriving emotional context of sensation in autonomous control systems. Open Cybern Syst J. 2007;1:28–46.
Tam D. EMOTION-II model: a theoretical framework for happy emotion as a self-assessment measure indicating the degree-of-fit (congruency) between the expectancy in subjective and objective realities in autonomous control systems. Open Cybern Syst J. 2007;1:47–60.
Tam ND. EMOTION-III model. A theoretical framework for social empathic emotions in autonomous control systems. Open Cybern Syst J. 2016 (in press).
Tam ND: Quantification of fairness bias in relation to decisions using a relativistic fairness-equity model. Adv in Soc Sci Research J 2014, 1(4):169-178.
Tam ND. A decision-making phase-space model for fairness assessment. Psychol Behav Sci. 2014;3(6–1):8–15.
P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique
Nicoladie D. Tam1, Luca Pollonini2, George Zouridakis3
1Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA; 2College of Technology, the University of Houston, TX, 77204, USA; 3Departments of Engineering Technology, Computer Science, and Electrical and Computer Engineering, University of Houston, Houston, TX, 77204, USA
Correspondence: Nicoladie D. Tam - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):P3
We aim to extract the intentional movement directions of the hemodynamic signals recorded from noninvasive optical imaging technique, such that a brain-computer-interface (BCI) can be built to control a wheelchair based on the optical signals recorded from the brain. Real-time detection of neurodynamic signals can be obtained using functional near-infrared spectroscopy (fNIRS), which detects both oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) levels in the underlying neural tissues. In addition to the advantage of real-time monitoring of hemodynamic signals using fNIRS over fMRI (functional magnetic resonance imaging), fNIRS also can detect brain signals of human subjects in motion without any movement artifacts. Previous studies had shown that hemodynamic responses are correlated with the movement directions based on the temporal profiles of the oxy-Hb and deoxy-Hb levels [1–5]. In this study, we will apply a phase space analysis to the hemodynamic response to decode the movement directions instead of using the temporal analysis in the previous studies.
Methods In order to decode the movement directions, human subjects were asked to execute two different orthogonal directional movements in the front-back and right-left directions while the optical hemodynamic responses were recorded in the motor cortex of the dominant hemisphere. We aim to decode the intentional movement directions without a priori any assumption on how arm movement directions are correlated with the hemodynamic signals. Therefore, we used the phase space analysis to determine how the trajectories of oxy-Hb and deoxy-Hb are related to each other during these arm movements.
Results The results show that there are subpopulations of cortical neurons that are task-related to the intentional movement directions. Specifically, using phase space analysis of the oxy-Hb and deoxy-Hb levels, opposite movement direction is represented by the different hysteresis of the trajectories in opposite direction in the phase space. Since oxy-Hb represents the oxygen delivery and deoxy-Hb represents the oxygen extraction by the underlying brain tissues, the phase space analysis provides a means to differentiate the movement direction by the ratio between oxygen delivery and oxygen extraction. In other words, the oxygen demands in the subpopulation of neurons in the underlying tissue differ depending on the movement direction. This also corresponds to the opposite patterns of neural activation and deactivation during execution of opposite movement directions. Thus, phase space analysis can be used as an analytical tool to differentiate different movement directions based on the trajectory of the hysteresis with respect to the hemodynamic variables.
Tam ND, Zouridakis G. Optical imaging of motor cortical activation using functional near-infrared spectroscopy. BMC Neurosci. 2012;13(Suppl 1):P27.
Tam ND, Zouridakis G. Optical imaging of motor cortical hemodynamic response to directional arm movements using near-infrared spectroscopy. Int J Biol Eng. 2013;3(2):11–17.
Tam ND, Zouridakis G. Decoding of movement direction using optical imaging of motor cortex. BMC Neurosci. 2013; P380.
Tam ND, Zouridakis G. Temporal decoupling of oxy- and deoxy-hemoglobin hemodynamic responses detected by functional near-infrared spectroscopy (fNIRS). J Biomed Eng Med Imaging. 2014;1(2):18–28.
Tam ND, Zouridakis G. Decoding movement direction from motor cortex recordings using near-infrared spectroscopy. In: Infrared spectroscopy: theory, developments and applications. Hauppauge: Nova Science; 2014.
Jaehyun Soh1, DaeEun Kim1
1Biological Cybernetics, School of Electrical and Electronic Engineering, Yonsei University, Shinchon, Seoul, 120-749, South Korea
Correspondence: DaeEun Kim - firstname.lastname@example.org
BMC Neuroscience 2016, 17(Suppl 1):P4
Weakly electric fish use electric field generated by the electric organ in the tail of the fish. They detect objects by sensing the electric field with electroreceptors on the fish’s body surface. Obstacles in the vicinity of the fish distort the electric field generated by the fish and the fish detect this distortion to recognize environmental situations. Generally, weakly electric fish produce species-dependent electric organ discharge (EOD) signals. Frequency bands of the fish’s signals include a variety of frequencies, 50–600 Hz or higher than 800 Hz. The EOD signals can be disturbed by similar frequency signals emitted by neighboring weakly electric fish. They change their EOD frequencies to avoid jamming signals when they detect the interference of signals. This is called jamming avoidance response (JAR).
The method of how to avoid jamming has been studied for a long time, but the corresponding neural mechanisms have not been revealed yet so far. The JAR of Eigenmannia can be analyzed by Lissajous graphs which consist of amplitude modulations and differential phase modulations. Relative intensity of signals at each skin can show that the signal frequency is higher than its own signal frequency or lower .
We suggest an algorithm of jamming avoidance for EOD signals, especially for wave-type fish. We explore the diagram of amplitude modulation versus phase modulation, and analyze the shape over the graph. The phase differences or amplitude differences will contribute to the estimation of the signal jamming situation. From that, the jammed signal frequency can be detected and so it can guide the jamming avoidance response. It can provide a special measure to predict the jamming avoidance response. However, what type of neural structure is available in weakly electric fish is an open question. We need further study on this subject.
Acknowledgements: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2014R1A2A1A11053839).
Heiligenberg W. Electrolocation of objects in the electric fish eigenmannia (rhamphichthyidae, gymnotoidei). J Comp Physiol. 1973;87(2):137–64.
Heiligenberg W. Principles of electrolocation and jamming avoidance in electric fish. Berlin: Springer; 1977.
Heiligenberg W. Neural nets in electric fish. Cambridge: MIT Press; 1991.
Minsu Yoo1, S. E. Palmer1,2
1Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA; 2Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
Correspondence: Minsu Yoo - email@example.com
BMC Neuroscience 2016, 17(Suppl 1):P5
Recent work has shown that retina ganglion cells (RGC) of salamanders predict future sensory information . It has also been shown that these RGC’s carry significant information about the future state of their own population firing patterns . From the perspective of downstream neurons in the visual system that do not have independent access to the visual scene, the correlations in the RGC firing, itself, may be important for predicting the future visual input. In this work, we explore the structure of the generalized correlation in firing patterns in the RGC, with a particular focus on coding efficiency. From the perspective of efficient neural coding, we might expect neurons to code for their own future state independently (decorrelation across cells), and to have very little predictive information extending forward in time (decorrelation in the time domain).
The results in this study may provide useful information for building a model of the RGC population that can explain why redundant coding is only observed at short delays, or what makes one RGC more predictive than another. Building this type of model will illustrate how the retina represents the future.
Palmer SE, Marre O, Berry MJ, Bialek W. Predictive information in a sensory population. Proc. Natl. Acad. Sci. 2015;112:6908–13.
Salisbury J, Palmer SE. Optimal prediction and natural scene statistics in the retina. ArXiv150700125 Q-Bio [Internet]. 2015 [cited 2016 Feb 25]; Available from: http://arxiv.org/abs/1507.00125.
Panzeri S, Senatore R, Montemurro MA, Petersen RS. Correcting for the sampling bias problem in spike train information measures. J. Neurophysiol. 2007;98:1064–72.