Biphasic action potentials in an individual cellular neural network cell

Hardware circuit that can effectively simulate biological neurons is an important basis for neuromorphic computation. Cellular neural network (CNN) cell is the basic information processor of a CNN, which acts like a neuron in the brain and has good circuit realizability. An individual memristive CNN cell is constructed by using a memristor instead of a linear resistor for imitating the ion channel time-varying conductance, in which abundant biphasic chaotic and periodic spiking activities are uncovered. This provides a new way to simulate biological neurons at the level of analog circuits. This paper first deduces the mathematical model of the memristive CNN cell, analyzes the equilibrium stability and then explores its dynamical behaviors based on numerical simulation. The results display that the different spiking activities can be effectively regulated by the system parameters and excitation parameters. Furthermore, the analog circuit of the memristive CNN cell is designed and the PSpice-based circuit simulations are performed to verify the correctness of the numerical simulations.