Bifurcation analysis and circuit implementation for a tabu learning neuron model

This paper investigates two-dimensional non-autonomous tabu learning neuron model. The equilibrium state is modulated by an externally applied input and the stability transitions of the stable and unstable equilibrium states via fold and Hopf bifurcations are disclosed. Complex neuron firing activities such as chaotic/periodic spiking/bursting firing patterns and coexisting bi-stable firing patterns are revealed by several numerical measures. Moreover, a nonlinear approximation scheme is presented to implement the neuron model using analog discrete components, which is achieved by utilizing an approximation activation function to replace the original activation function. Based on the circuit implementation, Multisim circuit simulations are performed to validate the feasibility of nonlinear approximation scheme and correctness of the numerical simulations. (C) 2020 Elsevier GmbH. All rights reserved.