The Relationship Between Synchronization Among Neuronal Populations and Their Mean Activity Levels

Abstract

In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The relationship between synchronization among neuronal populations and the population ring rates addresses two important issues: the distinction between rate coding and synchronization coding models of neuronal interactions and the degree to which empirical measurements of population activity, such as those employed by neuroimaging, are sensitive to changes in synchronization. We examined the relationship between mean population activity and synchronization using biologically plausible simulations. In this article, we focus on continuous stationary dynam ics. (In a companion article, Chawla (forthcoming), we address the same issue using stimulus-evoked transients.) By manipulating parameters such as extrinsic input, intrinsic noise, synaptic ef cacy, density of extrinsic connections, the voltage-sensitive nature of postsynaptic mechanisms, the number of neurons, and the laminar structure within the populations, we were able to introduce variations in both mean activity and synchronization under a variety of simulated neuronal architectures. Analyses of the simulated spike trains and local eld potentials showed that in nearly every domain of the model's parameter space, mean activity and synchronization were tightly coupled. This coupling appears to be mediated by an increase in synchronous gain when effective membrane time constants are lowered by increased activity. These observations show that under the assumptions implicit in our models, rate coding and synchrony coding in neural systems with reciprocal interconnections are two perspectives on the same underlying dynamic. This suggests that in the absence of speci c mechanisms decoupling changes in synchronization from ring levels, indexes of brain activity that are based purely on synaptic activity (e.g., functional magnetic resonance imaging) may also be sensitive to changes in synchronous coupling.

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

“…These findings add to an increasing body of literature suggesting that neuronal interactions result in both in phase and amplitude coupling (Bayraktaroglu et al, 2013;Chawla et al, 1999Chawla et al, , 2000Daffertshofer and van Wijk, 2011;Mehrkanoon et al, 2014). On the other hand, amplitude and phase coupling are considered to have different causes and putative functions (Engel et al, 2013), and to operate at different time scales .…”

Forward and backward connections in the brain: A DCM study of functional asymmetries

“…We had expected to see the converse given empirical results (e.g., Canolty et al 2006) and the simulations reported in Friston (2001). However, on reflection, the current results are entirely sensible if one considers that high (gamma) frequencies reflects increased neuronal firing (Chawla et al 1999): Heuristically, this means that gamma activity in low-level areas induces slower dynamics at higher cortical levels as prediction error is accumulated for perceptual synthesis. The concomitant high-level gamma activity (due to intrinsic nonlinear coupling) then accelerates the decay of evoked responses in the lower level that are manifest at, the population level, as damped alpha oscillations.…”

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

“…Computational modelling suggests that, as activity levels increase, smaller membrane time constants promote synchronous gain in the network, i.e. cells become more sensitive to temporal coincidences in their synaptic inputs, thus responding more to synchronous than to asynchronous inputs (Chawla, Lumer and Friston, 1999).…”