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

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