Volume 9 Supplement 1

Seventeenth Annual Computational Neuroscience Meeting: CNS*2008

Open Access

From multiple neural cortical networks to motor mechanical behavior: the importance of inherent learning over separable space-time length scales

  • Kanuresh Ganguly1,
  • Elizabeth B Torres2Email author,
  • Jorge V José3 and
  • Jose M Carmena4
BMC Neuroscience20089(Suppl 1):P70

DOI: 10.1186/1471-2202-9-S1-P70

Published: 11 July 2008

An important question in neuroscience is how different cortical areas bind during the planning and execution of voluntary, goal-directed behavior. Learning visually-guided reaches can provide important theoretical and experimental insights into this problem, particularly when combined with Brain-Machine Interface (BMI) and multi-electrode measurements over extended periods of time across multiple cortical regions. We exploit the force-field paradigm [1] that alters the arm dynamics of the subject to monitor the ensuing adaptive processes in order to understand across multiple regions the differences between a habitual reach and a reach that requires learning. We quantify the translation of movement plans into their physical implementation by studying the representation of time [2] in relation of its well documented separability from the spatial components of motion trajectories [3]. Previously the internal representation of environmentally-dependent forces on position and velocity was found to be time-invariant [2]. We aim at explaining this feature in relation to the motor system's plasticity [4] during closed loop BMI. To this end we followed the evolution of tuning, mean firing rate levels and spike-time statistics across separable cell classes simultaneously recorded in the pre-motor and motor cortical regions of rhesus macaques as they adapted to new movement dynamics imposed by an external mechanical device.

We find that (1) several stable spatio-temporal representations co-exist in a given cell which permits identification and selection of different motor programs to operate the external device, and (2) these multiple representations can be extracted from the multi-electrode neuron spike patterns reflecting various spatial re-parameterizations compatible with the ones imposed by the external mechanical device. A neural theoretical formulation in terms of a Hodgkin-Huxley excitatory and inhibitory neural ring network is used [5] to model multi-electrode spiking statistics, explicitly considering the separation of different motor dynamical times.

Declarations

Acknowledgements

Funding Sources NIH, NSF

Authors’ Affiliations

(1)
Neurology, UCSF
(2)
Biology-CNS, CALTECH
(3)
Physics, Physiology and Biophysics, SUNY
(4)
Computer Science, Electrical Engineering, UC Berkeley

References

  1. Shadmehr R, Mussa-Ivaldi FA: Adaptive representation of dynamics during learning of a motor task. J Neurosci. 1994, 14: 3208-3224.PubMedGoogle Scholar
  2. Conditt MA, Mussa-Ivaldi FA: Central Representation of time during motor learning. Proc Natl Acad Sci USA. 1999, 96: 11625-11630. 10.1073/pnas.96.20.11625.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Torres EB, Andersen RA: Space-time separation during obstacle avoidance learning in monkeys. J Neurophysiol. 2006, 96: 2613-2632. 10.1152/jn.00188.2006.View ArticlePubMedGoogle Scholar
  4. Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MAL: Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology. 2003, 1: 193-208. 10.1371/journal.pbio.0000042.View ArticleGoogle Scholar
  5. Tiesinga PHE, Fellous JM, Salinas E, Jose JV, Sejnowski TJ: Inhibitory Synchrony as a mechanism for attentional gain modulation. J Physiol (Paris). 2004, 98: 296-314. 10.1016/j.jphysparis.2005.09.002.View ArticleGoogle Scholar

Copyright

© Ganguly et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd.

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