Research on the Robustness Mechanism of Maeda–Makino Hardware Neuron Based on Symbolic Dynamics

Abstract

Noise permeates every level of the nervous systems, from single neurons to the whole system. However, the noise seems not to take so much trouble to neural information processing in contrast to the electronic system. The robustness of neural information processing inspired us to study the method of neuromorphic hardware systems to resist electromagnetic interference. The dynamics of the neuron model can reflect the inherent mechanism of neural information processing. In this work, we take the Maeda-Makino (MM) hardware neuron as an example to study the robustness mechanism of biological neuron information encoding based on symbolic dynamics. MM hardware neuron is a neuromorphic neuron circuit with biological plausibility. The simulation results show that MM hardware neuron can encode the order of current stimulus periods, even with parameter disturbance and noisy input signals which simulate the effects of electromagnetic interference on neuronal cell membrane permeability, sodium ion channels, potassium ion channels, and action potentials. Choosing a robust hardware neuron is the basis for building a neuromorphic hardware system that can resist electromagnetic interference. This letter not only verifies the robustness of using symbol sequence encoding but also provides an optional hardware neuron solution for neuromorphic hardware designed for anti-electromagnetic interference.