Flexible Frequency Switching in Adult Mouse Visual Cortex Is Mediated by Competition Between Parvalbumin and Somatostatin Expressing Interneurons

Abstract

Neuronal networks in rodent primary visual cortex (V1) can generate oscillations in different frequency bands depending on the network state and the level of visual stimulation. High-frequency gamma rhythms, for example, dominate the network's spontaneous activity in adult mice but are attenuated upon visual stimulation, during which the network switches to the beta band instead. The spontaneous local field potential (LFP) of juvenile mouse V1, however, mainly contains beta rhythms and presenting a stimulus does not elicit drastic changes in network oscillations. We study, in a spiking neuron network model, the mechanism in adult mice allowing for flexible switches between multiple frequency bands and contrast this to the network structure in juvenile mice that lack this flexibility. The model comprises excitatory pyramidal cells (PCs) and two types of interneurons: the parvalbumin-expressing (PV) and the somatostatinexpressing (SOM) interneuron. In accordance with experimental findings, the pyramidal-PV and pyramidal-SOM cell subnetworks are associated with gamma and beta oscillations, respectively. In our model, they are both generated via a pyramidal-interneuron gamma (PING) mechanism, wherein the PCs drive the oscillations. Furthermore, we demonstrate that large but not small visual stimulation activates SOM cells, which shift the frequency of resting-state gamma oscillations produced by the pyramidal-PV cell subnetwork so that beta rhythms emerge. Finally, we show that this behavior is obtained for only a subset of PV and SOM interneuron projection strengths, indicating that their influence on the PCs should be balanced so that they can compete for oscillatory control of the PCs. In sum, we propose a mechanism by which visual beta rhythms can emerge from spontaneous gamma oscillations in a network model of the mouse V1; for this mechanism to reproduce V1 dynamics in adult mice, balance between the effective strengths of PV and SOM cells is required.

Rate and oscillatory switching dynamics of a multilayer visual microcircuit model

“…We followed the approach of Mejias et al, 2016 and modeled each neuronal population with a single WC equation. Note however that despite this simplification our results on the switching dynamics and transition between different frequency bands remain qualitatively similar to studies that have used either multiple WC neurons per cell type ( Hertäg and Sprekeler, 2019 ) or implemented networks of multiple interneuron types with spiking neuron models ( Lee, 2018 ; Domhof and Tiesinga, 2021 ). The term denotes the input from other neuron populations across the entire microcircuit and is given by: …”

“…While the computational properties of simplified circuits with multiple interneuron types have been investigated theoretically ( Lee et al, 2017 ; Hertäg and Sprekeler, 2019 ; Garcia Del Molino et al, 2017 ; Lee, 2018 ), in the context of vision, locomotion, prediction errors, and whole-brain models ( Lee and Mihalas, 2017 ; Dipoppa et al, 2018 ; Hertäg and Sprekeler, 2020 ; Bensaid et al, 2019 ), the dynamics of more complex networks comprising multiple layers with translaminar connectivity remain unexplored. Moreover, even though models have examined the emergence of oscillations in local ( Lee, 2018 ; Domhof and Tiesinga, 2021 ; Veit et al, 2017 ) and whole-brain neuronal networks ( Bensaid et al, 2019 ) composed of canonical microcircuits, it is unclear how firing rate descriptions of microcircuit function relate to oscillatory behavior of cortical networks, which can differ across cortical layers ( Bastos et al, 2018 ; Adesnik, 2018 ; van Kerkoerle et al, 2014 ). This is crucial to interpret meso- and macroscopic signals from local field potential (LFP), electroencephalography (EEG) and magnetencephalography (MEG) recordings with respect to circuit function, where access to firing rate information is not possible.…”

Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex

“…Similar reservations hold for the mechanism by which oscillations are generated, such as for instance ING versus PING (Whittington et al, 2000;Tiesinga and Sejnowski, 2009;Tiesinga, 2012 and Buzsaki, 1996;White et al, 1998;Tiesinga and Jose, 2000; show that this would be feasible. Model 2 is comprised of two competing E-I motifs, which our recent simulations indicate (Domhof and Tiesinga, 2021) could implement switches when one motif is more strongly activated than the other. Our simulations do not exclude the possibility that the I1 population synchronizes by the ING mechanism, but it would in our opinion represent a less parsimonious explanation.…”

“…They form two PING-type motifs similarly to (Domhof and Tiesinga, 2021), which focused on beta/gamma frequency switches (see 4. Discussion).…”

The role of gamma oscillations in central nervous system diseases: Mechanism and treatment

“…In addition to the classic PING and ING gamma oscillations models, the three-cell-type motifs can generate theta-nested PING and beta-nested ING gamma oscillations (Ter Wal and Tiesinga, 2021). Moreover, the dynamic shift of oscillatory pattern in V1 following visual stimulation, from gamma to beta band, is the result of competition between the projective strengths of PV + and SST + interneurons (Domhof and Tiesinga, 2021). Collectively, these findings suggest that the multi-interneuron network 10.3389/fncel.2022.962957 flexibly modulates PCs firing and consequently controls the onset and switching of brain oscillations.…”