Dynamic circuit motifs underlying rhythmic gain control, gating and integration

Abstract

Brain circuitry processes information by rapidly and selectively engaging functional neuronal networks. The dynamic formation of networks is often evident in rhythmically synchronized neuronal activity and tightly correlates with perceptual, cognitive and motor performances. But how synchronized neuronal activity contributes to network formation and how it relates to the computation of behaviorally relevant information has remained difficult to discern. Here we structure recent empirical advances that link synchronized activity to the activation of so-called dynamic circuit motifs. These motifs explicitly relate (1) synaptic and cellular properties of circuits to (2) identified timescales of rhythmic activation and to (3) canonical circuit computations implemented by rhythmically synchronized circuits. We survey the ubiquitous evidence of specific cell and circuit properties underlying synchronized activity across theta, alpha, beta and gamma frequency bands and show that their activation likely implements gain control, context-dependent gating and state-specific integration of synaptic inputs. This evidence gives rise to the dynamic circuit motifs hypothesis of synchronized activation states, with its core assertion that activation states are linked to uniquely identifiable local circuit structures that are recruited during the formation of functional networks to perform specific computational operations.

Theta–gamma coordination between anterior cingulate and prefrontal cortex indexes correct attention shifts

“…4). In previous studies, 5-to 10-Hz activity fluctuations were shown to organize distinct band-limited gamma-frequency bands categorized as low (∼35-55 Hz), medium (∼50-90 Hz), and high (epsilon; ∼90-140 Hz) bands, each likely originating in separable underlying circuit motifs (8,11,26). The observation that ACC/ PFC circuits theta-synchronized the activation at a low-gammafrequency band is, to our knowledge, unprecedented in LFP recordings in the primate brain.…”

“…A rich set of predominantly rodent studies have documented such interareal neuronal interactions in the form of a phase-amplitude (P-A) correlations between lowfrequency periodic excitability fluctuation and high-frequency gamma-band activity (7)(8)(9). It is, however, unknown whether there are reliable cross-frequency P-A interactions between those primate ACC/PFC nodes that underlie flexible attention shifts and, if so, whether P-A correlations are reliably linked to the actual successful deployment of attention (10,11). We thus set out to test for and characterize P-A interactions during covert control processes by recording local field potential (LFP) activity in macaque ACC/PFC subfields during attentional stimulus selection.…”

A model for attentional information routing through coherence predicts biased competition and multistable perception

“…This setup can be seen as an extended implementation of the dynamical network motif of feed-forward inhibition, which is frequently encountered in local cortical, cotico-cortical, and thalamo-cortical cicuits (Womelsdorf et al 2014). The most stripped down version of this motif only has an inhibitory connection onto the excitatory subpopulation and common input driving both excitatory and inhibitory subpopulations.…”

Neural mechanisms of mismatch negativity (MMN) dysfunction in schizophrenia

“…By contrast, we 34 and others 35,36,65 have linked MMN deficits to impaired theta-frequency response, and underlying NMDAR dysfunction in rodent 66,67 and primate 28 models. At the local circuit level, theta rhythms are tied to interactions involving non-PV cells, especially somatostatintype inhibitory interneurons 6,29,68 . The present replication of prior theta-frequency findings thus further supports models of impaired function across interneuron types in schizophrenia 6 .…”

“…In ERSP analysis, electrophysiological activity is divided conventionally into discrete delta (.5-4 Hz), theta (4-7 Hz), alpha (7)(8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) and gamma (>24 Hz) bands, which reflect differential underlying local-circuit processes 20,[27][28][29] . Within these bands, stimulus-related activity is further differentiated into those that reflect alterations in phase reset mechanisms as reflected in intertrial coherence (ITC) vs. those that reflect alterations in single-trial power (e.g.…”