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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.

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Figure 1: Dynamic circuit motifs and the structural basis of several motifs with identified timescales.
Figure 2: Canonical computations of cortical circuits and generic response modulations supported by specific cell types.
Figure 3: Dynamic circuit motifs activated at alpha, low beta and gamma frequencies that implement gain controls, gating and integration of inputs.
Figure 4: Gating of distal and proximal dendritic information streams through rhythmic inhibition at theta frequencies in the hippocampus and at 10–20 Hz in the neocortex.

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Acknowledgements

We are grateful to the speakers of two workshops held at the Computational and Systems Neuroscience (cosyne.org) meetings on The Consequences of Brain Rhythms in the Organization of Neuronal Computation (2009) and Developing Simplified Algebras to Describe Large-Scale Brain Dynamics (2011) for numerous discussions. We thank T. Donner, C. Eliasmith, B. Hansen and M. Vinck for discussions and comments on an earlier version of the manuscript. T.W. was supported by grants from the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Ontario Ministry of Economic Development and Innovation (MEDI). T.A.V. was supported by grants from the CIHR. N.T.S. was supported by US National Institutes of Health (NIH) R01 grants NS18741, NS44623 and grant HD 18381, NIH Institutional Training Grant T32 MH070328, and the US National Center for Research Resources (P41 RR14075). P.T. was supported by a grant from the Netherlands Organization for Scientific Research (NWO) Computational Lifesciences program and by Neuroseeker (FP7 grant agreement 600925).

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Supplementary Figure 1 Thalamo-cortical alpha rhythmic motif.

(a) The structural connectivity (top) shows that the alpha rhythmic motif depends on rhythmically bursting, high threshold (HT) cells entraining inhibitory neurons (IN), which in turn impose rhythmic suppression onto relay-mode, regular spiking projection neurons. The bursting frequency of the HT cells depends on intrinsic currents, whereas the synchrony derives from coupling by gap junctions. Depending on whether the IN cells fire single spikes or burst, the relay cells are either in-phase suppressed or anti-phase suppressed. The bottom panels shows the cross correlograms illustrating the rhythmic activation of the interneurons (Int) by bursting HT cells (left) and the alpha rhythmic inhibition of relay mode cells by an interneurons (right). (b) Example activation traces showing the relation of interneuron IPSPs (bottom) on relay mode neuron spike generation (middle) and the extracellular recorded filtered LFP (top). The phase-of-firing histogram (right) shows that during alpha rhythmic pulsed inhibition relay neuron firing is facilitated particularly in the third quarter of the oscillation cycle. Adapted from Lorincz, M.L., Kekesi, K.A., Juhasz, G., Crunelli, V. & Hughes, S.W. Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm. Neuron 63, 683-696, copyright Elsevier (2009).

Supplementary Figure 2 Selective gamma coherence during selective visual processing.

(a) The spatial coverage of ECoG electrode locations (dots) projected on the rendered cortical surface of a macaque brain. Dots indicate the 252 electrodes of a high density ECoG grid. Green and red dots indicate electrodes recording activity over V1 and V4, respectively. ‘V1a’ and ‘V1b’ denote two V1 electrode locations recording non-overlapping receptive fields. (b) Stimulus arrangement for the two attention conditions was identical. In one condition attention was directed to the stimulus that activated V4 and the V1a site (stimulus circled in red for illustration purposes). In the other condition attention was directed to the stimulus that activated V4 and the V1b site (stimulus circled in blue for illustration purposes). (c) Spectral power change relative to pre-stimulus baseline (upper panel) in V1 in the two attention conditions (red and blue). The bottom panel shows the coherence spectra of the V4 to V1a and V1b (red and blue) when the attended stimulus overlaid V1a (red) and V1b (blue). (d) Illustration of the attention condition (left). Bottom-Up Granger causal influence for the V1a to V4 (red) and the V1b to V4 (blue) connections when the attended stimulus overlaid with V4 and V1a. The rightmost panel shows the respective Top-Down Granger causal influence. (e) Same format as (d) but for the condition with attention on the stimulus that activated the V4 and the V1b recording site. Gray bars in (c,d,e) indicate the frequencies with a significant effect (p<0.05, corrected for multiple comparisons across frequencies, non-parametric randomization across site pairs). Adapted from Bosman, C.A., et al. Attentional Stimulus Selection through Selective Synchronization between Monkey Visual Areas. Neuron 75, 875-888, copyright Elsevier (2012).

Supplementary Figure 3 Visual attention and selective ‘push-pull’ gating in visual cortex at gamma band frequencies mediated by putative interneurons.

(a) Normalized action potential waveforms of single cells recorded in area V4. (b) Distribution of peak-to-trough durations of action potentials across neurons reveals a bimodal distribution that separate narrow spiking from broad spiking cells. (c) Spike-LFP phase locking (measured as pairwise phase consistency) across cells in V4 for narrow and broad spiking cells shows significant gamma band synchronization for NS and BS cells, and significant alpha band synchronization for NS cells. (d,e) Attention (attend inside, PPCin, versus outside, the visual receptive field of the cells, PPCout) decreases gamma band phase locking for NS and BS cells that show low firing rates (d), and increases phase locking for cells with higher firing rates (e). The respective gamma bands are highlighted with grey bars in the top of the panels. This finding suggests a push-pull mechanism of attention that implements the up- and down-modulation of cells’ synchronization depending on their overall rate in response to visual stimuli. Low/High firing rate cells were median split. Adapted from Vinck, M., Womelsdorf, T., Buffalo, E.A., Desimone, R. & Fries, P. Attentional modulation of cell-class-specific gamma-band synchronization in awake monkey area v4. Neuron 80, 1077-1089, copyright Elsevier (2013).

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Womelsdorf, T., Valiante, T., Sahin, N. et al. Dynamic circuit motifs underlying rhythmic gain control, gating and integration. Nat Neurosci 17, 1031–1039 (2014). https://doi.org/10.1038/nn.3764

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