Discrete Modeling of Multi-Transmitter Neural Networks with Neuron Competition

TL;DR

This paper introduces a discrete, computationally efficient model of central pattern generators that emphasizes nonsynaptic interactions and neuron competition to explain rhythmic activity in nervous systems.

Contribution

It presents a novel discrete neuron model incorporating competition and nonsynaptic interactions, advancing understanding of rhythmic pattern generation.

Findings

Neuronal competition is key to rhythm generation.

Model successfully simulates half-center oscillator.

Application to snail feeding network demonstrates model's validity.

Abstract

We propose a novel discrete model of central pattern generators (CPG), neuronal ensembles generating rhythmic activity. The model emphasizes the role of nonsynaptic interactions and the diversity of electrical properties in nervous systems. Neurons in the model release different neurotransmitters into the shared extracellular space (ECS) so each neuron with the appropriate set of receptors can receive signals from other neurons. We consider neurons, differing in their electrical activity, represented as finite-state machines functioning in discrete time steps. Discrete modeling is aimed to provide a computationally tractable and compact explanation of rhythmic pattern generation in nervous systems. The important feature of the model is the introduced mechanism of neuronal competition which is shown to be responsible for the generation of proper rhythms. The model is illustrated with two examples: a half-center oscillator considered to be a basic mechanism of emerging rhythmic activity and the well-studied feeding network of a pond snail. Future research will focus on the neuromodulatory effects ubiquitous in CPG networks and the whole nervous systems.