{"title":"BioNN:利用非线性多时间尺度混合反馈控制硬件上的生物仿真神经网络,实现神经调节爆发节奏","authors":"Kangni Liu;Shahin Hashemkhani;Jonathan Rubin;Rajkumar Kubendran","doi":"10.1109/JETCAS.2023.3330084","DOIUrl":null,"url":null,"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.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BioNN: Bio-Mimetic Neural Networks on Hardware Using Nonlinear Multi-Timescale Mixed-Feedback Control for Neuromodulatory Bursting Rhythms\",\"authors\":\"Kangni Liu;Shahin Hashemkhani;Jonathan Rubin;Rajkumar Kubendran\",\"doi\":\"10.1109/JETCAS.2023.3330084\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":48827,\"journal\":{\"name\":\"IEEE Journal on Emerging and Selected Topics in Circuits and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal on Emerging and Selected Topics in Circuits and Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10308577/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10308577/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
BioNN: Bio-Mimetic Neural Networks on Hardware Using Nonlinear Multi-Timescale Mixed-Feedback Control for Neuromodulatory Bursting Rhythms
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.
期刊介绍:
The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.