Firing Cell: An Artificial Neuron with a Simulation of Long-Term-Potentiation-Related Memory

TL;DR

This paper introduces a novel artificial neuron model called firing cell that simulates complex neural phenomena including memory-related plasticity, and demonstrates diverse chaotic output behaviors through computer simulations.

Contribution

The firing cell model uniquely integrates multiple neural properties and plasticity mechanisms, advancing the realism of computational neuron simulations.

Findings

The model exhibits various chaotic behaviors depending on input phase.

It simulates long-term and short-term synaptic potentiation effects.

The neuron can be excitatory, inhibitory, or receptive.

Abstract

We propose a computational model of neuron, called firing cell (FC), properties of which cover such phenomena as attenuation of receptors for external stimuli, delay and decay of postsynaptic potentials, modification of internal weights due to propagation of postsynaptic potentials through the dendrite, modification of properties of the analog memory for each input due to a pattern of short-time synaptic potentiation or long-time synaptic potentiation (LTP), output-spike generation when the sum of all inputs exceeds a threshold, and refraction. The cell may take one of the three forms: excitatory, inhibitory, and receptory. The computer simulations showed that, depending on the phase of input signals, the artificial neuron's output frequency may demonstrate various chaotic behaviors.