Resolution enhancement in neural networks with dynamical synapses

TL;DR

This paper explores how neural networks with dynamical synapses can enhance resolution in processing multiple stimuli by leveraging temporal activity modulations, beyond traditional spike rate coding.

Contribution

It demonstrates that dynamical synapses enable neural networks to improve resolution of stimuli through temporal modulation, extending understanding of neural information processing.

Findings

Resolution enhancement depends on synaptic depression dynamics

Temporal modulations improve discrimination of similar stimuli

Conditions for optimal information processing are discussed

Abstract

Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.