A multiple timescales approach to bridging spiking- and population-level dynamics

TL;DR

This paper develops a multiple timescales theoretical framework to connect slow synaptic mean field dynamics with the synchronization behavior of finite neural populations, bridging microscopic spiking activity and macroscopic population phenomena.

Contribution

It introduces a novel approach using multiple timescales and averaging theory to predict synchronization from mean field dynamics in heterogeneous neural populations.

Findings

Accurately predicts phase drift and locking in theta neuron networks.

Successfully applies theory to Traub and Wang-Buzs{á}ki models.

Handles non-trivial mean-field dynamics with heterogeneous inputs.

Abstract

A rigorous bridge between spiking-level and macroscopic quantities is an on-going and well-developed story for asynchronously firing neurons, but focus has shifted to include neural populations exhibiting varying synchronous dynamics. Recent literature has used the Ott--Antonsen ansatz (2008) to great effect, allowing a rigorous derivation of an order parameter for large oscillator populations. The ansatz has been successfully applied using several models including networks of Kuramoto oscillators, theta models, and integrate-and-fire neurons, along with many types of network topologies. In the present study, we take a converse approach: given the mean field dynamics of slow synapses, predict the synchronization properties of finite neural populations. The slow synapse assumption is amenable to averaging theory and the method of multiple timescales. Our proposed theory applies to two heterogeneous populations of N excitatory n-dimensional and N inhibitory m-dimensional oscillators with homogeneous synaptic weights. We then demonstrate our theory using two examples. In the first example we take a network of excitatory and inhibitory theta neurons and consider the case with and without heterogeneous inputs. In the second example we use Traub models with calcium for the excitatory neurons and Wang-Buzs{\'a}ki models for the inhibitory neurons. We accurately predict phase drift and phase locking in each example even when the slow synapses exhibit non-trivial mean-field dynamics.