Synergistic pathways of modulation enable robust task packing within neural dynamics

TL;DR

This paper investigates how different neuromodulatory mechanisms in recurrent neural networks enable robust multi-task learning by enhancing task packing efficiency and managing context ambiguity through synergistic modulation pathways.

Contribution

It introduces a detailed analysis of two forms of contextual modulation—neuronal excitability and synaptic strength—and demonstrates their complementary roles in multi-task learning within neural dynamics.

Findings

Modulation mechanisms improve robustness to context ambiguity.

Synergistic pathways enhance multi-task packing efficiency.

Distinct neuronal dynamics induced by different modulation types.

Abstract

Understanding how brain networks learn and manage multiple tasks simultaneously is of interest in both neuroscience and artificial intelligence. In this regard, a recent research thread in theoretical neuroscience has focused on how recurrent neural network models and their internal dynamics enact multi-task learning. To manage different tasks requires a mechanism to convey information about task identity or context into the model, which from a biological perspective may involve mechanisms of neuromodulation. In this study, we use recurrent network models to probe the distinctions between two forms of contextual modulation of neural dynamics, at the level of neuronal excitability and at the level of synaptic strength. We characterize these mechanisms in terms of their functional outcomes, focusing on their robustness to context ambiguity and, relatedly, their efficiency with respect to packing multiple tasks into finite size networks. We also demonstrate distinction between these mechanisms at the level of the neuronal dynamics they induce. Together, these characterizations indicate complementarity and synergy in how these mechanisms act, potentially over multiple time-scales, toward enhancing robustness of multi-task learning.