Piecewise-Linear Simplification for Adaptive Synaptic Neuron Model

Adaptive synaptic neuron model involves complex activation functions. These nonlinearities lead to complicated hardware implementations, which greatly hinder neuron-based applications. To effectively solve this issue, a piecewise-linear (PWL) activation function with simplified circuit implementation is presented for the adaptive synaptic neuron model in this brief. With this neuron model, the stability evolution mechanism of the equilibrium state is analyzed and the parameter- and initial condition-related neuron dynamics are numerically explored. Afterwards, an analog circuit is designed and manually made using commercially available components. The phase trajectories captured by the hardware experiments verify the feasibility of the PWL activation function. Thus, such a PWL simplification shows superiority in emulating neuron dynamics.

Automated summary

The complex activation functions in the adaptive synaptic neuron model result in complicated hardware implementations, which hinder its applications. This paper presents a piecewise-linear activation function with simplified circuit implementation, which proves its superiority in emulating neuron dynamics through hardware experiments.