  Cortical neurons emit seemingly erratic trains of action potentials or
"spikes," and neural network dynamics emerge from the coordinated spiking
activity within neural circuits. These rich dynamics manifest themselves in a
variety of patterns, which emerge spontaneously or in response to incoming
activity produced by sensory inputs. In this Review, we focus on neural
dynamics that is best understood as a sequence of repeated activations of a
number of discrete hidden states. These transiently occupied states are termed
"metastable" and have been linked to important sensory and cognitive functions.
In the rodent gustatory cortex, for instance, metastable dynamics have been
associated with stimulus coding, with states of expectation, and with decision
making. In frontal, parietal, and motor areas of macaques, metastable activity
has been related to behavioral performance, choice behavior, task difficulty,
and attention. In this article, we review the experimental evidence for neural
metastable dynamics together with theoretical approaches to the study of
metastable activity in neural circuits. These approaches include (i) a
theoretical framework based on non-equilibrium statistical physics for network
dynamics; (ii) statistical approaches to extract information about metastable
states from a variety of neural signals; and (iii) recent neural network
approaches, informed by experimental results, to model the emergence of
metastable dynamics. By discussing these topics, we aim to provide a cohesive
view of how transitions between different states of activity may provide the
neural underpinnings for essential functions such as perception, memory,
expectation, or decision making, and more generally, how the study of
metastable neural activity may advance our understanding of neural circuit
function in health and disease.
