Neural Computation, Vol 9, 607-621, Copyright © 1997 by The MIT Press
Self-organization of firing activities in monkey's motor cortex: trajectory computation from spike signals
S Lin, J Si and AB Schwartz
Department of Electrical Engineering, Arizona State University, Tempe 85287, USA.
The population vector method has been developed to combine the simultaneous
direction-related activities of a population of motor cortical neurons to
predict the trajectory of the arm movement. In this article, we consider a
self-organizing model of a neural representation of the arm trajectory
based on neuronal discharge rates. As self- organizing feature map (SOFM)
is used to select the optimal set of weights in the model to determine the
contribution of an individual neuron to an overall movement representation.
The correspondence between movement directions and discharge patterns of
the motor cortical neurons is established in the output map. The topology-
preserving property of the SOFM is used to analyze the recorded data of a
behaving monkey. The data used in this analysis were taken while the monkey
was tracing spirals and doing center-->out movements. The arm trajectory
could be well predicted using such a statistical model based on the motor
cortex neuronal firing information. The SOFM method is compared with the
population vector method, which extracts information related to trajectory
by assuming that each cell has a fixed preferred direction during the task.
This implies that these cells are acting along lines labeled only for
direction. However, extradirectional information is carried in these cell
responses. The SOFM has the capability of extracting not only
direction-related information but also other parameters that are
consistently represented in the activity of the recorded population of
cells.
