Cortical computations via metastable activity

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. Highlights• Metastable activity is increasingly being observed in many cortical areas and in a variety of tasks.• Metastable activity is correlated with sensory and cognitive processes and may subserve state-dependent computations.• Metastable activity can emerge spontaneously in spiking networks with clustered architecture.• Metastable activity is a candidate neural substrate for coding both external events and internal representations.

Muscarinic-Dependent miR-182 and QR2 Expression Regulation in the Anterior Insula Enables Novel Taste Learning

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Instability with a purpose: how the visual brain makes decisions in a volatile world

“…What might be the neural basis of the bistable variables / 'local attractors' proposed here? Ongoing activity in sensory cortex appears to be low-dimensional, in the sense that the activity of neurons with similar response properties varies concomitantly ["shared vari- Possible dynamical origins of shared and moderately slow variability have been studied extensively in theory and simulation [for reviews, see 47,58,77]. Networks with weakly clustered connectivity (e.g., 3% rewiring) can express a metastable attractor dynamics with moderately long time-scales [36,67,101,103].…”

Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information

“…However, it fails to explain the trial-to-trial neural variability in terms of spike counts, behavioral variability in terms of reaction times, and switching between metastable states observed in cortical activity across various tasks and species. Over recent years, balanced random networks have been extended to capture neural trial-to-trial variability and multistability by accommodating cluster topology in the network architecture (Litwin-Kumar & Doiron, 2012;Deco & Hugues, 2012;Mazzucato et al, 2015Mazzucato et al, , 2019La Camera et al, 2019). These studies use purely excitatory clustering and neglect a possible structure in the topology of local inhibitory networks.…”