BioNN: Bio-mimetic Neural Networks on Hardware using Nonlinear Multi-timescale Mixed-feedback Control for Neuromodulatory Bursting Rhythms

• Published in: IEEE journal on emerging and selected topics in circuits and systems Vol. 13; no. 4; p. 1
• Main Authors: Liu, Kangni, Hashemkhani, Shahin, Vivekanand, Vijay Shankaran, Xiong, Feng, Rubin, Jonathan, Kubendran, Rajkumar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Behavioral sciences; Bifurcations; bio-inspired; Biological neural networks; Biological system modeling; Biology; Bursting; coupled neural networks; Current sources; Dynamical systems; Feedback control; Hardware; Integrated circuit modeling; Locomotion; Mathematical models; mixed-feedback control; Neural networks; neuromorphic; Neurons; Neurophysiology; Nonlinear control; Nonlinear dynamics; Parameters; Synapses; Waveforms
• ISSN: 2156-3357; 2156-3365
• DOI: 10.1109/JETCAS.2023.3330084

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.