Volume 10 Supplement 1

Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access

Interplay between spontaneous and sensory activities in barrel cortex: a computational study

  • Elena Phoka1Email author,
  • Mark Wildie1,
  • Rasmus S Petersen2,
  • Mauricio Barahona1 and
  • Simon R Schultz1
BMC Neuroscience200910(Suppl 1):P351

DOI: 10.1186/1471-2202-10-S1-P351

Published: 13 July 2009

Introduction

The observation that cortical neuronal responses to repeated application of the same stimulus have a high degree of "trial to trial" variability has led to the notion that neuronal responses are affected by the intrinsic spontaneous state of the system and are not solely a direct reflection of the sensory input. Complementing this, a number of sources of recent evidence have suggested that ongoing ("spontaneous") spatiotemporal patterns of activity do not merely reflect stochastic network fluctuations and internal noise sources, but can be affected by the recent history of sensory experience. This evidence includes multi-electrode array recordings, voltage sensitive dye imaging [1, 2], as well as electron-microscopic evidence of a change in inhibitory synapse density and concomitant physiological changes after a substantial period of whisker stimulation [3]. These observations lead to some interesting questions. Is the spontaneous functional connectivity network itself modified by sensory-evoked activity? And if so, what mechanisms could account for it, and what information processing tasks might such a phenomenon help to perform? We are specifically testing a hypothesis that varieties of spike timing dependent plasticity (STDP) that have been previously documented can account for this effect.

To address these questions, we have developed a biologically inspired model of (initially) a single barrel, consisting of approximately 2000 neurons (Izhikevich point neuron model, [4]). We model layers 2/3 and 4 each representing a 200 μm × 200 μm × 200 μm cube of tissue. The connectivity is random with connection probabilities for each neuron class (layer 2/3: excitatory pyramidal, and inhibitory basket and non-basket; layer 4: excitatory spiny stellate) constrained by data from the physiological literature. STDP occurs at the excitatory synapses. Sensory evoked activity is generated by direct input to layer 4 neurons from a Linear-Nonlinear-Poisson (LNP) model of thalamic nucleus VPm with experimentally recorded thalamic transfer functions [5]. Our model accounts for spontaneous activity in the barrel cortex and allows us to generate specific testable predictions concerning the effect of temporally patterned whisker stimulation on spontaneous spatiotemporal dynamics in barrel cortex. This model may help us to generate and refine hypotheses concerning the role of ongoing network activity in memory consolidation and perceptual learning.

Declarations

Acknowledgements

This work was supported by BBSRC.

Authors’ Affiliations

(1)
Department of Bioengineering, Imperial College London
(2)
Faculty of Life Sciences, University of Manchester

References

  1. Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A: Spontaneously emerging cortical representations of visual attributes. Nature. 2004, 425: 954-956. 10.1038/nature02078.View ArticleGoogle Scholar
  2. Han F, Caporale N, Dan Y: Reverbation of recent visual experience in spontaneous cortical waves. Neuron. 2008, 60: 321-327. 10.1016/j.neuron.2008.08.026.PubMed CentralPubMedView ArticleGoogle Scholar
  3. Knott GW, Quairiaux C, Genoud C, Welker E: Formation of dendritic spines with GABAergic synapses induced by whisker stimulation in adult mice. Neuron. 2002, 34: 265-273. 10.1016/S0896-6273(02)00663-3.PubMedView ArticleGoogle Scholar
  4. Izhikevich EM, Edelman GM: A large-scale model of mammalian thalamocortical systems. PNAS. 2008, 105: 3593-3598. 10.1073/pnas.0712231105.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Petersen RS, Brambilla M, Alenda A, Bale MR, Montemurro MA, Panzeri S, Maravall M: Diverse and temporally precise kinetic feature selectivity in the VPm thalamic nucleus. Neuron.
  6. Petersson KM: Artificial grammar learning and neural networks. Proc Cogn Sci Soc. 2005, 1726-1731.Google Scholar
  7. Reber AS: Implicit learning of artificial grammar. J Verb Learn & Verb Behav. 1967, 6: 855-863. 10.1016/S0022-5371(67)80149-X.View ArticleGoogle Scholar
  8. Çürüklü B: A Canonical Model of the Primary Visual Cortex. 2005, Mälardalen University PressGoogle Scholar
  9. Mountcastle VB: The columnar organization of the neocortex. Brain. 1997, 701-722. 10.1093/brain/120.4.701.Google Scholar
  10. Gewaltig MO, Diesmann M: NEST. Scholarpedia. 2007, 2: 1430.View ArticleGoogle Scholar

Copyright

© Phoka et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.

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