Superlinear Population Encoding of Dynamic Hand Trajectory in Primary
Motor Cortex
The Journal of Neuroscience, (2004)Abstract
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.
Description
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