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.



Funding Sources NIH, NSF

Authors’ Affiliations

Neurology, UCSF
Physics, Physiology and Biophysics, SUNY
Computer Science, Electrical Engineering, UC Berkeley


  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


© Ganguly et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd.