Superlinear population
encoding of dynamic hand trajectory in primary motor cortex
Liam Paninski, Shy Shoham, Matthew Fellows,
Nicholas Hatsopoulos, and John Donoghue
Appeared as
Journal
of Neuroscience 24: 8551-8561.
Neural activity in primary motor cortex (MI) is known to correlate
with hand position and velocity. Previous descriptions of this tuning
have a) been linear in position or velocity, b) depended only
instantaneously on these signals, and/or c) not incorporated the
effects of interneuronal dependencies on firing rate. We show here
that many MI cells encode a nonlinear function of the full
time-varying hand trajectory. Roughly 20% of MI cells carry
information in the hand trajectory beyond just the position, velocity,
and acceleration at a single time lag. Moreover, roughly one third of
MI cells encode the trajectory in a significantly superlinear fashion;
as one consequence, even small position changes can dramatically
modulate the gain of MI cells' velocity tuning, in agreement with
recent psychophysical evidence (Hwang et al. '03). We introduce a
compact nonlinear filter model that predicts the complex structure of
the spatiotemporal tuning functions described in earlier work
(Paninski et al. '04). Finally, observing the activity of neighboring
cells in the MI network significantly increases the predictability of
a single MI cell's firing rate; however, we find interneuronal
dependencies in MI to be much more locked to external kinematic
parameters than those recently described in the hippocampus (Harris et
al. '03). 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.
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