A spiking implementation of the lamprey's Central Pattern Generator in neuromorphic VLSI

Abstract

The lamprey has been often used as a model for understanding the role of Central Pattern Generators (CPGs) in locomotion. Many artificial neural network models have been proposed in the past to explain the neuro-physiology and behavioral data measured from the lamprey, and several robotic implementations have been built to test the software models in a real physical bio-mimetic artifact, and to reproduce the characteristic locomotion patterns observed in the real lamprey. However, in these systems there has typically been a clear separation between the mechanical component of the system (the body), and its control part (the CPG), typically implemented with conventional digital platforms, such as micro-controllers or Field Programmable Gate Arrays (FPGAs). Here we propose to implement a CPG network using neuromorphic electronic circuits, that can be directly interfaced to the robotic actuators of a bio-mimetic robotic lamprey, eliminating the distinction between software and hardware. These circuits comprise low-power analog silicon neurons and synapses, that are affected by device mismatch and noise. The challenge is therefore to determine the CPG model that can best implement robust locomotion control of the bio-mimetic artifact, in face of the constraints imposed by the neuromorphic implementation. As these constraints are similar to the ones faced by the neurons and synapses in the real lamprey (e.g., finite and small power consumption, finite and small resolution or signal to noise ratio, large variability, etc.), the final system implementation will shed light onto the neural processing principles used by real CPG networks to produce robust and distributed control of locomotion in a physical bio-mimetic artifact.