Integration of continuous-time dynamics in a spiking neural network simulator

TL;DR

This paper introduces a unified simulation framework that integrates continuous-time rate-based models with spiking neural networks, enabling multi-scale modeling, validation, and increased reliability in neural simulations.

Contribution

It presents a novel implementation allowing simultaneous simulation of rate-based and spiking models within a single framework, enhancing flexibility and applicability.

Findings

Supports combined spiking and rate-based neural models

Enables validation of mean-field approaches with spiking simulations

Demonstrates applicability across various neural network models

Abstract

Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units. The unified simulation framework presented here supports the combination of the two for multi-scale modeling approaches, the quantitative validation of mean-field approaches by spiking network simulations, and an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most efficient spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. We further demonstrate the broad applicability of the framework by considering various examples from the literature ranging from random networks to neural field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.