Attractor Dynamics with Synaptic Depression

TL;DR

This paper explores how short-term synaptic depression influences continuous attractor neural networks, revealing enhanced tracking, slow-decay plateau behaviors, and implications for short-term memory mechanisms.

Contribution

It demonstrates that STD enables static and traveling bumps, improves input tracking, and introduces slow-decaying plateau states in CANNs, offering insights into neural short-term memory.

Findings

STD allows static and traveling bump formations.

STD enhances the network's ability to track external inputs.

STD induces slow-decaying plateau behaviors in neural activity.

Abstract

Neuronal connection weights exhibit short-term depression (STD). The present study investigates the impact of STD on the dynamics of a continuous attractor neural network (CANN) and its potential roles in neural information processing. We find that the network with STD can generate both static and traveling bumps, and STD enhances the performance of the network in tracking external inputs. In particular, we find that STD endows the network with slow-decaying plateau behaviors, namely, the network being initially stimulated to an active state will decay to silence very slowly in the time scale of STD rather than that of neural signaling. We argue that this provides a mechanism for neural systems to hold short-term memory easily and shut off persistent activities naturally.