BioNN: Bio-Mimetic Neural Networks on Hardware Using Nonlinear Multi-Timescale Mixed-Feedback Control for Neuromodulatory Bursting Rhythms

Abstract

Biological neurons exhibit rich and complex nonlinear dynamics, which are computationally expensive and area/power hungry for hardware implementation. This paper presents a mathematical analysis and hardware realization of neural networks using a nonlinear neuron model that utilizes two excitable systems operating at different timescales. The neuron consists of a mixed-feedback system operating at multiple timescales to exhibit a variety of modalities that resemble the biophysical mechanisms found in neurophysiology. The single neuron dynamics emerge from four voltage-controlled current sources and feature spiking and bursting output modes that can be controlled using tunable parameters. The bifurcation structures of the neuron, modeled as a 4D dynamical system, illustrate the roles of sources acting on different timescales in shaping neural dynamics. A comprehensive understanding of the system’s dynamic behavior is obtained by studying the state space variables and performing bifurcation analysis on the different parameters. The model is implemented to a 1mm x 2mm prototype chip utilizing the 180nm CMOS process. Each neural network consists of 1 isolated test neuron and 5 fully connected neurons using 20 synapses. By carefully selecting bias voltages according to the I-V characterization curves, the neurons are shown to exhibit spike, burst, and burst excitable behavior. Multiple small-scale neural networks with inhibitory or excitatory synapses were verified to achieve coupled rhythms with neuron bursts in-phase or out-of-phase. To demonstrate an application, the generated burst waveforms from the 4-neuron network were used to form a Central Pattern Generator (CPG) for locomotion control of the four legs of the Petoi, a quadruped robot, enabling the bot to jump successfully.