Transitions among metastable states underlie context-dependent working memories in a multiple timescale network

TL;DR

This paper presents a biologically plausible neural network model demonstrating how metastable state transitions support context-dependent working memories through slow and fast neural dynamics, ensuring stability and robustness.

Contribution

The study introduces a novel neural network mechanism that stabilizes task-related states in a context-dependent manner using simple Hebbian learning, differing from prior models.

Findings

Single or few states are stabilized per epoch, enhancing robustness.

The model maintains performance despite noise and protocol changes.

It demonstrates a biologically plausible basis for complex working memory processes.

Abstract

Transitions between metastable states are commonly observed in the neural system and underlie various cognitive functions such as working memory. In a previous study, we have developed a neural network model with the slow and fast populations, wherein simple Hebb-type learning enables stable and complex (e.g., non-Markov) transitions between neural states. This model is distinct from a network with asymmetric Hebbian connectivity and a network trained with supervised machine learning methods: the former generates simple Markov sequences. The latter generates complex but vulnerable sequences against perturbation and its learning methods are biologically implausible. By using our model, we propose and demonstrate a novel mechanism underlying stable working memories: sequentially stabilizing and destabilizing task-related states in the fast neural dynamics. The slow dynamics maintain a history of the applied inputs, e.g., context signals, and enable the task-related states to be stabilized in a context-dependent manner. We found that only a single (or a few) state(s) is stabilized in each epoch (i.e., a period in the presence of the context signal and a delayed period) in a working memory task, resulting in a robust performance against noise and change in a task protocol. These results suggest a simple mechanism underlying complex and stable processing in neural systems.