In recent years, devices capable of linking the brain to the external
world have been developed. Such devices directly measure the output
of multiple neurons simultaneously. One obvious application of this
technology is in helping those who are movement impaired; in theory
it would be possible to implant such a device in the brain, and use
its output to control movement of a robotic prosthetic limb,
for instance. However, to achieve this goal, it is necessary to first
understand the relationship between movement and neural signals.
We consider data collected from rhesus monkeys in experiments, and
propose a model for describing this relationship. The model generalizes
several previously-considered models from the neuroscience literature,
and allows individual neurons to (1) encode different kinematic
variables, and (2) to have more general spike count distributions. The
proposed model is used to decode cortical signals recorded for 258
neurons in the ventral premotor cortex of rhesus monkeys during an
ellipse-drawing task, and we demonstrate that relative to the existing
models, a substantial reduction in mean squared error is achieved.
Keywords: Bayesian decoding, neuron, spike, model selection