Implementation of Excitatory CMOS Neuron Oscillator for Robot Motion Control Unit

Abstract

Abstract-This paper presents an excitatory CMOS neuron oscillator circuit design, which can synchronize two neuron-bursting patterns. The excitatory CMOS neuron oscillator is composed of CMOS neurons and CMOS excitatory synapses. And the neurons and synapses are connected into a close loop. The CMOS neuron is based on the HindmarshRose (HR) neuron model and excitatory synapse is based on the chemical synapse model. In order to fabricate using a 0.18 um CMOS standard process technology with 1.8V compatible transistors, both time and amplitude scaling of HR neuron model is adopted. This full-chip integration minimizes the power consumption and circuit size, which is ideal for motion control unit of the proposed bio-mimetic micro-robot. The experimental results demonstrate that the proposed excitatory CMOS neuron oscillator performs the expected waveforms with scaled time and amplitude. The active silicon area of the fabricated chip is 1.1 mm 2 including I/O pads.

A Survey of Neuromorphic Computing and Neural Networks in Hardware

“…2) Biologically-Inspired: There are a variety of neuron models that are simplified versions of the Hodgkin-Huxley model that have been implemented in hardware, including Fitzhugh-Nagumo [62]- [64] and Hindmarsh-Rose [65]- [69] models. These models tend to be both simpler computationally and simpler in terms of number of parameters, but they become more biologically-inspired than biologically-plausible because they attempt to model behavior rather than trying to emulate physical activity in biological systems.…”

“…Many of the requirements for robotics, including motor control, are applications that have been successfully demonstrated in neural networks. Some of the common applications of neuromorphic systems for robotics include learning a particular behavior [2548], [2549], locomotion control or control of particular joints to achieve a certain motion [67]- [69], [361], [663], [1082], [1083], [1261], [1279], [1527], [1784], [1885], [2550], [2551], social learning [2552], [2553], and target or wall following [498], [606], [1576], [1682], [2554]. Thus far, in terms of robotics, the most common use of neuromorphic implementations is for autonomous navigation tasks [16], [195], [393], [486], [532], [547], [735], [1077], [1329]- [1333], [1339], [1503], [1539], [1563], [1671], [1716], [2379], [2555]- [2560].…”