Exact neural mass model for synaptic-based working memory

TL;DR

This paper introduces an exact neural mass model that captures the dynamics of synaptic-based working memory, linking cellular mechanisms to macroscopic brain signals and providing insights into capacity and oscillatory activity.

Contribution

The authors develop a neural mass model that exactly reproduces heterogeneous spiking network dynamics, bridging cellular mechanisms with observable brain signals during working memory tasks.

Findings

Model reproduces WM-related oscillations in $eta-\gamma$ band.

Memory capacity depends on presentation rate, with an analytic expression derived.

Mean membrane potential correlates with memory load, similar to event potentials.

Abstract

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to get insight on Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are associated to stimulus-locked transient oscillations followed by a steady-state activity in the $\beta-\gamma$ band, thus resembling what observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the $\gamma$ power increases with the number of loaded items, as reported in many experiments, while $\theta$ and $\beta$ power reveal non monotonic behaviours.