Mathematical and Circuit Level Analysis Interpretation and Recommendations on Neuron Models

Abstract

Contrary to von Neumann computer architecture, neuromorphic computing is a biologically inspired method for building several sorts of brain-inspired computers. This computer technology uses silicon neurons and synapses to solve difficult machine learning and AI challenges. The goal of neuromorphic computing is to build a brain-like ability to compute, learn, and adapt. Building an appropriate neuroscience model, establishing a new architecture, modeling new devices, finding new materials for the devices, programming framework, and applications for these neuromorphic devices are major technological challenges. This study covers numerous neuron models from Hodgkin–Huxley to I&F, mathematical equations, circuit level analysis and motivations for neuromorphic computing. Here some of the most useful silicon neuron models are discussed in terms of biological plausibility, computational efficiency, and temperature dependency. This survey shows that more than 52 transistors make up the silicon HH neuron, which occupies less than 0.01[Formula: see text][Formula: see text] and consumes 60[Formula: see text]uW of power. On the other hand, an I&F neuron needs less than 20 transistors, occupies 442[Formula: see text][Formula: see text] and consumes 40[Formula: see text]pW of power.