Using Firing-Rate Dynamics to Train Recurrent Networks of Spiking Model Neurons

TL;DR

This paper introduces a method to train recurrent spiking neural networks by leveraging continuous-variable rate models to generate target patterns, enabling the spiking networks to perform complex dynamical tasks with minimal neuron expansion.

Contribution

The authors present a novel training procedure that uses rate models to guide the development of recurrent spiking networks capable of autonomous dynamical behaviors.

Findings

Spiking networks can replicate tasks of rate models with slight neuron increase.

The approach bridges rate models and spiking neural networks for dynamical pattern generation.

The method enhances understanding of neural computation and representation.

Abstract

Recurrent neural networks are powerful tools for understanding and modeling computation and representation by populations of neurons. Continuous-variable or "rate" model networks have been analyzed and applied extensively for these purposes. However, neurons fire action potentials, and the discrete nature of spiking is an important feature of neural circuit dynamics. Despite significant advances, training recurrently connected spiking neural networks remains a challenge. We present a procedure for training recurrently connected spiking networks to generate dynamical patterns autonomously, to produce complex temporal outputs based on integrating network input, and to model physiological data. Our procedure makes use of a continuous-variable network to identify targets for training the inputs to the spiking model neurons. Surprisingly, we are able to construct spiking networks that duplicate tasks performed by continuous-variable networks with only a relatively minor expansion in the number of neurons. Our approach provides a novel view of the significance and appropriate use of "firing rate" models, and it is a useful approach for building model spiking networks that can be used to address important questions about representation and computation in neural systems.