Chapter 16 Connectionist contributions to population coding in the motor cortex

Abstract

This chapter discusses population coding in the motor cortex. Single unit activity, accounted for input-output transforms in distributed neural networks. Serial recording during a stereotyped task has no demerits in understanding population coding, assuming a reasonable neuronal sample, and a hypothesis related to relatively stereotyped input-output mappings. Complex, emergent features of neuronal ensembles is not needed for an understanding of input-output mappings, but may play a role in more fundamental information-processing functions, such as learning, coordination among parallel networks, and attention to action. The population vector is computed from the activity of hidden units, but its accuracy does not depend on direct influences of hidden units on output elements. Cosine-like tuning and population vector coding emerged from a minimal-assumption network. The population vector rotated when the input output function is changed from the standard mapping to a nonstandard one. Population-vector rotation is accounted by a transition between two inputs, one representing the visual signal and reflecting standard visuomotor mapping, and the target signal into which it is transformed, and reflecting nonstandard mapping. The changes of hidden-unit tuning properties that occurred while the network learned a nonstandard transform corresponded to those observed in the motor cortex of a monkey that learned analogous transforms. The tuning properties of individual cells changed during the learning of both transformational and arbitrary mappings. During learning, the population vector became more accurate in both model and biological networks, for both standard and nonstandard mappings, and for both transformational and arbitrary nonstandard mappings.