Network mechanisms of working memory: the role of neuronal nonlinearities

TL;DR

This paper investigates how neuronal nonlinearities, especially transfer function saturation, contribute to working memory maintenance and erasure in neural networks, supported by both rate and spiking neuron models.

Contribution

It introduces a novel mechanism where specific neuronal transfer function relations enable memory preservation without neuron saturation and allows memory erasure via global signals.

Findings

Neuronal nonlinearities can sustain working memory through structured connectivity.

A relation between excitatory and inhibitory transfer functions enables effective saturation.

Memory can be erased at the end of delay by a global excitatory signal.

Abstract

The oculomotor delayed-response (ODR) task is a common experimental paradigm of working memory (WM) study, in which a monkey must fixate its gaze on the center of a screen and, following a brief cue that flashes on the screen, keep fixating for several more seconds before shifting its gaze to the location where the cue flashed. Consequently, in the delay period between the cue and the response the monkey must maintain a memory of cue location. Electrophysiological recordings from the prefrontal area of the cortex (PFC) revealed neurons that display selective persistent activity: their firing rate change induced by the cue persists through delay period, but only in response to a confined range of cue locations. This suggests that the representation of the cue is maintained in the network by a change in network activity profile. In this work, we study a network of rate-model neurons that is capable of preserving information about a past input, owing to structured connectivity and nonlinearities in the neuronal transfer function (TFs). Particularly, we focus on the acceleration of the TF close to firing threshold and the concavity around TF saturation. Any memory mechanism which exploits TF saturation means that some neurons must fire close to their saturation rates; with our model, however, we show that a certain relation between the excitatory and inhibitory neurons' TFs can cause an effective saturation in the network without forcing the neurons into the saturating parts of their TFs. In addition, this mechanism enables the erasure of memory at the end of the delay by a global excitatory signal. Finally, we demonstrate the mechanism in a model network of spiking neurons which describes with more detail the oscillatory dynamics in the state transition due to the interaction of membrane and synaptic time constants which is neglected in the rate model.