Integrating Non-spiking Interneurons in Spiking Neural Networks.

Beck Strohmer,Poramate Manoonpong,Leon Bonde Larsen,Rasmus Karnøe Stagsted

doi:10.3389/fnins.2021.633945

Beck Strohmer, Poramate Manoonpong + Show 2 more

Open Access

https://doi.org/10.3389/fnins.2021.633945

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Abstract

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

Highlights

Current research employing neural networks for locomotion control tends to focus on homogeneous networks of neurons communicating through either graded signals (Aoi et al, 2017) or action potentials (Bing et al, 2018)
Depolarizing currents received by non-spiking interneuron (NSI) are shown to reset biological central pattern generator rhythms (Bidaye et al, 2018)
The results confirm our model of an NSI is capable of shaping output and setting rhythmic patterns of an spiking central pattern generator (sCPG) network based on a changing analog input

Summary

Introduction

Current research employing neural networks for locomotion control tends to focus on homogeneous networks of neurons communicating through either graded signals (Aoi et al, 2017) or action potentials (Bing et al, 2018). Sensor neurons receive information from the external environment and pass it onto NSIs through current injections (Bidaye et al, 2018). This data is sent onwards by the NSI, affecting the membrane potential of the connected neurons through a graded signal (Burrows and Siegler, 1978). They are found to be the primary neuronal type in some animals such as the C. elegans where communication through graded potentials is the Integrating Non-spiking Interneurons main transmission method (Schafer, 2016).

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