Spatiotemporal tuning properties for hand position
and velocity in motor cortical neurons
Liam Paninski, Matthew Fellows,
Nicholas Hatsopoulos, and John Donoghue
Appeared as
Journal of Neurophysiology 91: 515-532
A pursuit tracking task (PTT) and multielectrode recordings were used
to investigate the spatiotemporal encoding of hand position and
velocity in primate primary motor cortex (MI). Continuous tracking of
a randomly moving visual stimulus provided a broad sample of velocity
and position space, reduced statistical dependencies between kinematic
variables, and minimized the nonstationarities that are found in
typical "step-tracking" tasks. These statistical features permitted
the application of signal-processing and information-theoretic tools
for the analysis of neural encoding. The multielectrode method allowed
for the comparison of tuning functions among simultaneously recorded
cells. During tracking, MI neurons showed heterogeneity of position
and velocity coding, with markedly different temporal dynamics for
each. Velocity tuned neurons were approximately sinusoidally tuned for
direction, with linear speed scaling; other cells showed sinusoidal
tuning for position, with linear scaling by distance. Velocity
encoding led behavior by ~100 ms for most cells, while position tuning
was more broadly distributed, with leads and lags suggestive of both
feedforward and feedback coding. Individual cells encoded velocity and
position weakly, with comparable amounts of information about
each. Linear regression methods confirmed that random, two-dimensional
hand trajectories can be reconstructed from the firing of small
ensembles of randomly selected neurons (3-19 cells) within the MI arm
area. These findings demonstrate that MI carries information about
evolving hand trajectory during visually guided pursuit tracking,
including information about arm position both during and after its
specification. However, the reconstruction methods used here capture
only the low-frequency components of movement during the PTT. Hand
motion signals appear to be represented as a distributed code in which
diverse information about position and velocity is available within
small regions of MI.
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