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The Journal of Neuroscience, September 29, 2004, 24(39):8551-8561; doi:10.1523/JNEUROSCI.0919-04.2004

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Behavioral/Systems/Cognitive
Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex

Liam Paninski,¹ Shy Shoham,² Matthew R. Fellows,³ Nicholas G. Hatsopoulos,⁴ and John P. Donoghue³

¹Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom WC1N 3AR, ²Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, ³Department of Neuroscience, Brown University, Providence, Rhode Island 02912, and ⁴Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois 60637

Neural activity in primary motor cortex (MI) is known to correlate with hand position and velocity. Previous descriptions of this tuning have (1) been linear in position or velocity, (2) depended only instantaneously on these signals, and/or (3) not incorporated the effects of interneuronal dependencies on firing rate. We show here that many MI cells encode a superlinear function of the full time-varying hand trajectory. Approximately 20% of MI cells carry information in the hand trajectory beyond just the position, velocity, and acceleration at a single time lag. Moreover, approximately one-third of MI cells encode the trajectory in a significantly superlinear manner; as one consequence, even small position changes can dramatically modulate the gain of the velocity tuning of MI cells, in agreement with recent psychophysical evidence. We introduce a compact nonlinear "preferred trajectory" model that predicts the complex structure of the spatiotemporal tuning functions described in previous work. Finally, observing the activity of neighboring cells in the MI network significantly increases the predictability of the firing rate of a single MI cell; however, we find interneuronal dependencies in MI to be much more locked to external kinematic parameters than those described recently in the hippocampus. Nevertheless, this neighbor activity is approximately as informative as the hand velocity, supporting the view that neural encoding in MI is best understood at a population level.

Key words: hand; motor cortex; movement; motion; motor activity; gain modulation; statistical model; correlated coding

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Received March 12, 2004; revised August 10, 2004; accepted August 10, 2004.

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