Rhythm and Synchrony in a Cortical Network Model

Abstract

We studied mechanisms for cortical gamma-band activity in the cerebral cortex and identified neurobiological factors that affect such activity. This was done by analyzing the behavior of a previously developed, data-driven, large-scale network model that simulated many visual functions of monkey V1 cortex (Chariker et al., 2016). Gamma activity was an emergent property of the model. The model's gamma activity, like that of the real cortex, was (1) episodic, (2) variable in frequency and phase, and (3) graded in power with stimulus variables like orientation. The spike firing of the model's neuronal population was only partially synchronous during multiple firing events (MFEs) that occurred at gamma rates. Detailed analysis of the model's MFEs showed that gamma-band activity was multidimensional in its sources. Most spikes were evoked by excitatory inputs. A large fraction of these inputs came from recurrent excitation within the local circuit, but feedforward and feedback excitation also contributed, either through direct pulsing or by raising the overall baseline. Inhibition was responsible for ending MFEs, but disinhibition led directly to only a small minority of the synchronized spikes. As a potential explanation for the wide range of gamma characteristics observed in different parts of cortex, we found that the relative rise times of AMPA and GABA synaptic conductances have a strong effect on the degree of synchrony in gamma. Canonical computations used throughout the cerebral cortex are performed in primary visual cortex (V1). Providing theoretical mechanisms for these computations will advance understanding of computation throughout cortex. We studied one dynamical feature, gamma-band rhythms, in a large-scale, data-driven, computational model of monkey V1. Our most significant conclusion is that the sources of gamma band activity are multidimensional. A second major finding is that the relative rise times of excitatory and inhibitory synaptic potentials have strong effects on spike synchrony and peak gamma band power. Insight gained from studying our V1 model can shed light on the functions of other cortical regions.

Cortico-cortical feedback from V2 exerts a powerful influence over the visually evoked local field potential and associated spike timing in V1

“…The current consensus is that gamma rhythms are generated locally, within a cortical area, through recurrent loops involving fast spiking inhibitory interneurons (Börgers and Kopell, 2003;Buzsáki and Wang, 2012;Chariker et al, 2018;Headley and Paré, 2017;Tiesinga and Sejnowski, 2009). The fact that they can be produced in reduced preparations, such as cortical slices (Whittington et al, 1995), makes a strong argument for the sufficiency of local circuits.…”

Contrast response in a comprehensive network model of macaque V1

“…The results of Figure 2E through 2G are taken from Chariker et al (2018). The raster in Figure 2E depicts a gamma-band rhythm produced by a local population when optimally driven.…”

Correlations in population dynamics in multi-component networks

“…They measure the degree of synchrony of the population as a whole, without regard to which neurons fired which spikes or the level of correlated activity between specific pairs of neurons. SSI has similarities with spike field coherence (SFC), but differs because it considers spikespike synchrony while SFC considers spike-local field potential synchrony (Chariker et al 2018;Fries 2005;Chalk et al 2010;Fries et al 2001;Jia et al 2013).…”