Population coding in a neural net for trajectory formation

Abstract

In this study we investigate the time evolution of the activity in a topographically ordered neural network with external input for two types of neurons: one network with binary-valued neurons with a stochastic behaviour and one with deterministic neurons with a continuous output. We demonstrate that for a particular range of lateral interaction strengths, changes in external input give rise to gradual changes in the position of clustered neural activity.

The theoretical results are illustrated by computer simulations in which we have simulated a neural network model for trajectory planning for a multi-joint manipulator. The model gives a collision-free trajectory by combining the sensory information about the position of target and obstacles. The position of the manipulator is uniquely related to the clustered activity of the population of neurons, the population vector. The movement of the manipulator from any initial position to the target position is the result of the intrinsic dynamics of the network.