Spatial features of synaptic adaptation affecting learning performance

TL;DR

This paper explores how the spatial distribution of synaptic plasticity influences learning in neural networks, emphasizing the role of messenger molecule diffusion in mediating synaptic adaptation and optimizing learning performance.

Contribution

It introduces a neural learning model where plastic signals propagate extracellularly, highlighting the importance of spatial range and extent of synaptic adaptation for effective learning.

Findings

Fully excitatory networks achieve high learning performance.

Learning sensitivity depends on the spatial extent of synaptic plasticity.

Optimal plastic adaptation features enhance learning efficiency.

Abstract

Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation and the spatial range of synaptic connections.