Dynamic Updating of Distributed Neural Representations using Forward Models
In this paper, we present a continuous attractor network model, which we hypothesize will give some suggestion of the mechanisms underlying several neural processes, such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor-transformations,motor control, motor imagery and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies. With respect to them, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue to be relevant for a better understanding of the neural processes of the brain.
Record created on 2007-11-07, modified on 2016-08-08