Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning
Fig 1
Simulation of a velocity-tuning based model with variable neuron-kinematic latencies.
(a) Task design of a 13-direction center-out reaching task. The firing of a simulated neuron is plotted around the reach directions. (b) Two example neurons with differing latencies. (c) Principal components (PCs) for a simulated population of 200 neurons (latency SD: 72 ms, movement SD: 56 ms). (d) Exemplar jPCA plane for the first 6 PCs of the simulated population from 0 ms before to 200 ms after neural movement onset (analysis was computed on entire movement). Individual conditions are colored based on their activity at neural movement onset in the first jPC. (e) Proportion of change in neural trajectory explained by rotational dynamics (in all jPCA planes) for various latency offsets and movement durations. A value of 1 indicates that rotational dynamics completely explain the transformation between each time point and its temporal derivative.