Dynamic updating of distributed neural representations using forward models

Abstract

In this paper, we present a continuous attractor network model that 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 [593][594][595][596][597][598][599][600][601][602] 2003). With respect to these studies, 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 are relevant for a better understanding of the neural processes of the brain.

Robot Programming by Demonstration

“…Expanding on the concept of a MNS for sensorimotor coupling, Sauser & Billard [140,141] have explored the use of competitive neural fields to explain the dynamics underlying multimodal representation of sensory information and the way the brain may disambiguate and select across competitive sensory stimuli to proceed to a given motor program. This work explains the principle of ideomotor compatibility, by which "observing the movements of others influences the quality of one's own performance", and develops neural models which account for a set of related behavioral studies [142], see Figure 59.23.…”

Bimanual circling in deafferented patients: Evidence for a role of visual forward models

“…Forward models may be learned, updated, and fine‐tuned quite quickly (see, e.g. Sauser & Billard, 2006).…”

A Dynamic Neural Field Approach to the Covert and Overt Deployment of Spatial Attention

“…The (1), a discretized equation in time of the DNF, states that the evolution of a field u i depends on a relaxation term (-u i (t)), an external input (I i (t)), a baseline firing (h) as well as lateral interactions within the field (w ij ). While some dynamical properties have been demonstrated mathematically (formation of stable patterns [20], traveling waves [22]), this framework has also been applied successfully on several sensorimotor tasks [23][24][25][26].…”