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.

Figure 1. Observing metastability in neural populations.

(a) Schematics illustration of a hidden Markov model (HMM) with three states. Hidden states (squares) are collections of firing rates across neurons; arrows indicate transition rates among the states (thicker arrows denote larger transition rates). (b) HMM applied to electrophysiology recordings from the gustatory cortex of a behaving rat. The top panels show spike rasters from 10 simultaneously recorded neurons segmented via HMM analysis. Two trials are shown, one in which sucrose (left) and one in which quinine (right) was administered to the rat. HMM states were assigned to each bin of data when their posterior probability exceeded 0.8 (dashed horizontal line). The bottom panels show the state sequences in four different trials with either sucrose (left) or quinine (right) as a taste stimulus. Transitions may occur at variable times across trials, but the state sequences are reliable. Panel (b) modified from Ref. [13].

Figure 2. Metastable activity and cognitive function.

(a) Left: Multiunit activity from monkey V4 during a selective attention task alternates between ON (green) and OFF (pink) HMM states. Right: Behavioral performance improves when the decision occurs during ON intervals compared to OFF intervals. (b) Left: Probability of being in a state representing the value of the chosen (red), unchosen (blue) and unavailable (gray) option from monkey OFC ensembles in a decision-making task. Right panels: Comparison of time-course of state probability in quick versus deliberative decisions as judged by the dynamics of eye movements (depicted at top). (c) Left: Dynamics of HMM states decoded from hippocampal neural activity (top) and position (bottom) of a rat running along a linear track (six runs; only data during sharp wave ripples, comprising 2% of the session, were used). Right: Mapping latent state probabilities to associated animal positions yields latent-state place fields describing the probability of each state for every position on the track. (a) panels modified with permission from Ref. [14^••]; (b) panels modified with permission from Ref. [17^••]; (c) panels modified from Ref. [16^••].

Figure 3. Spiking network model of metastable activity.

(a) Spike rasters of a spiking network model of excitatory (E) and inhibitory (I) integrate-and-fire neurons with two different architectures. Left: homogeneous network. Right: clustered network. The homogeneous network has uniform connectivity among the E neurons. In the clustered network, the E neurons are organized in clusters, so that synaptic connectivity is stronger inside each cluster than among clusters. The spiking activity in the clustered network is metastable. Insets: graphical description of the topology of each network. (b) The mean field theory of a clustered spiking network with 30 E clusters shows coexistence of several attractors for intra-cluster synaptic weight (ICW) J₊ beyond the critical value 4.2 (dashed vertical green line; J₊ is in units of the baseline synaptic weights outside clusters). The curves represent the firing rates of neurons in each cluster according to the number of active clusters (numbers from 1 to 8). Grey and purple curves: E neurons; red curves: I neurons. A state on the purple curve is ‘globally inactive’ in the sense that no clusters are active and firing rates of E neurons remain low. (c) Raster plot from a typical simulation of the model in (b) with ICW corresponding to the full vertical green line. Thirty E neurons (one for each separate cluster) are shown, together with the segmentation of the rasters in HMM states. Panel (a) adapted with permission from Ref. [20^••]; panel (b) adapted from Ref. [54]; panel (c) adapted from Ref. [22^••].