# Cortical computations via metastable activity

## TL;DR

This paper reviews recent findings on metastable brain activity, highlighting its role in representing internal states and task variables through dynamic neural ensembles, shifting the perspective from trial-averaged to state-based coding.

## Contribution

It synthesizes recent experimental and theoretical work on metastable neural dynamics, emphasizing their mechanistic origins and functional significance in cortical processing.

## Key findings

- Metastable states are involved in internal representations like attention and decision-making.
- Neural activity unfolds as sequences of discrete metastable states in response to stimuli and internally generated processes.
- Theoretical models suggest mechanisms for the emergence of metastability in neural circuits.

## Abstract

Metastable brain dynamics are characterized by abrupt, jump-like modulations so that the neural activity in single trials appears to unfold as a sequence of discrete, quasi-stationary states. Evidence that cortical neural activity unfolds as a sequence of metastable states is accumulating at fast pace. Metastable activity occurs both in response to an external stimulus and during ongoing, self-generated activity. These spontaneous metastable states are increasingly found to subserve internal representations that are not locked to external triggers, including states of deliberations, attention and expectation. Moreover, decoding stimuli or decisions via metastable states can be carried out trial-by-trial. Focusing on metastability will allow us to shift our perspective on neural coding from traditional concepts based on trial-averaging to models based on dynamic ensemble representations. Recent theoretical work has started to characterize the mechanistic origin and potential roles of metastable representations. In this article we review recent findings on metastable activity, how it may arise in biologically realistic models, and its potential role for representing internal states as well as relevant task variables.