Implementation of compact VLSI FitzHugh-Nagumo neurons

Abstract

In this paper we show a low power and very compact VLSI implementation of a FitzHugh-Nagumo neuron for large network implementations. The circuit consists of only 17 small transistors and two capacitors and consumes less than 23 muW. It is composed of a nonlinear resistor and a lossy active inductor. We demonstrate that a simple low Q active inductor can be used instead of a complex one because the parasitic series resistor can be easily embedded to the FitzHugh-Nagumo model. We also perform a statistical analysis to check the robustness of the circuit against mismatch.