Dynamics of multi-stable states during ongoing and evoked cortical activity

TL;DR

This paper investigates the spontaneous and stimulus-evoked multi-stable states in cortical activity, proposing a neural network model that explains the emergence of multi-stability and its modulation by external stimuli.

Contribution

It introduces a recurrent spiking network model that reproduces spontaneous multi-stable states and their suppression during sensory stimulation, aligning with experimental data.

Findings

Spontaneous state sequences occur without external stimuli.

External stimuli reduce neuron multi-stability and variability.

Model accurately reproduces observed cortical dynamics.

Abstract

Single trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single neuron multi-stability represents a challenge to existing spiking network models, where typically each neuron is at most bi-stable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multi-stability in single neuron firing rates. Each state results from the activation of neural clusters with potentiated intra-cluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the models ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single neuron multi-stability into bi-stability and the reduction of trial-by-trial variability. Both predictions were confirmed in the data. Altogether, these results provide a theoretical framework that captures both ongoing and evoked network dynamics in a single mechanistic model.