How synapses can enhance sensibility of a neural network

TL;DR

This paper investigates how chemical synapses, modeled with delays and learning rules, can significantly increase the sensitivity of neural networks, with potential implications for understanding neural adaptability.

Contribution

It introduces a neuronal network model incorporating chemical synapses with delays and Hebbian learning, demonstrating their role in enhancing network sensibility.

Findings

Chemical synapses can abruptly increase network sensibility.

Learning rules further amplify the sensitivity enhancement.

Chemical synapses' effects are more pronounced with adaptive learning.

Abstract

In this work, we study the dynamic range in a neuronal network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.