Smooth nonlinear fitting scheme for analog multiplierless implementation of Hindmarsh-Rose neuron model

The Hindmarsh-Rose (HR) neuron model is built to describe the neuron electrical activities. Due to the polynomial nonlinearities, multipliers are required to implement the HR neuron model in analog. In order to avoid the multipliers, this brief presents a novel smooth nonlinear fitting scheme. We first construct two nonlinear fitting functions using the composite hyperbolic tangent functions and then implement an analog multiplierless circuit for the two-dimensional (2D) and three-dimensional (3D) HR neuron models. To exhibit the nonlinear fitting effects, numerical simulations and hardware experiments for the fitted HR neuron model are provided successively. The results show that the fitted HR neuron model with analog multiplierless circuit can display different operation patterns of resting, periodic spiking, and periodic/chaotic bursting, entirely behaving like the original HR neuron model. The analog multiplierless circuit has the advantage of low implementation cost and thereby it is suitable for hardware implementation of large-scale neural networks.

Automated summary

This paper introduces a novel smooth nonlinear fitting scheme to implement the HR neuron model in analog without using multipliers. By constructing nonlinear fitting functions and implementing analog multiplierless circuits, the nonlinear fitting effects of the adapted HR neuron model are successfully demonstrated.