Towards Scalable Digital Modeling of Networks of Biorealistic Silicon Neurons

Abstract

The study of biorealistic neuron circuits has been limited by the efficiency of digital implementations. Efficient digital approaches generally use I&F variants, losing important neural properties for network computation. In contrast, accurate neuron ODEs tend to utilize computationally intensive operations, causing the overhead to become prohibitive for large spiking neural network applications. This effort presents efficient digital approximations for coupled HH neurons derived from transistor-channel neural modeling. Neuron models are implemented in C using floating-point and 32-bit fixed-point arithmetic, and small networks are simulated using a fixed-step ODE solver. Our approach enables large network simulation of HH-like neurons, facilitating scalable digital modeling while also providing a direct path towards a framework for analog computation.