Presynaptic modulation as fast synaptic switching: state-dependent modulation of task performance

TL;DR

This paper proposes that presynaptic neuromodulation can enable rapid, state-dependent switching between different task modes in neural networks, allowing flexible adaptation without retraining.

Contribution

It introduces the concept of a low complexity modulation matrix that enables synaptic switching for different subtasks, enhancing network adaptability.

Findings

Networks can switch between trained subtasks using a modulation matrix.

Neuromodulation allows blending memory and novelty modes.

Networks can adapt to task demands without full retraining.

Abstract

Neuromodulatory receptors in presynaptic position have the ability to suppress synaptic transmission for seconds to minutes when fully engaged. This effectively alters the synaptic strength of a connection. Much work on neuromodulation has rested on the assumption that these effects are uniform at every neuron. However, there is considerable evidence to suggest that presynaptic regulation may be in effect synapse-specific. This would define a second "weight modulation" matrix, which reflects presynaptic receptor efficacy at a given site. Here we explore functional consequences of this hypothesis. By analyzing and comparing the weight matrices of networks trained on different aspects of a task, we identify the potential for a low complexity "modulation matrix", which allows to switch between differently trained subtasks while retaining general performance characteristics for the task. This means that a given network can adapt itself to different task demands by regulating its release of neuromodulators. Specifically, we suggest that (a) a network can provide optimized responses for related classification tasks without the need to train entirely separate networks and (b) a network can blend a "memory mode" which aims at reproducing memorized patterns and a "novelty mode" which aims to facilitate classification of new patterns. We relate this work to the known effects of neuromodulators on brain-state dependent processing.