Dynamics and Effective Topology Underlying Synchronization in Networks of Cortical Neurons

By Danny Eytan, Shimon Marom. Journal of Neuroscience, 2006

403 citations recorded by the source corpus.

Cognitive processes depend on synchronization and propagation of electrical activity within and between neuronal assemblies. In vivo measurements show that the size of individual assemblies depends on their function and varies considerably, but the timescale of assembly activation is in the range of 0.1–0.2 s and is primarily independent of assembly size. Here we use an in vitro experimental model of cortical assemblies to characterize the process underlying the timescale of synchronization, its relationship to the effective topology of connectivity within an assembly, and its impact on propagation of activity within and between assemblies. We show that the basic mode of assembly activation, “network spike,” is a threshold-governed, synchronized population event of 0.1–0.2 s duration and follows the logistics of neuronal recruitment in an effectively scale-free connected network. Accordingly, the sequence of neuronal activation within a network spike is nonrandom and hierarchical; a small subset of neurons is consistently recruited tens of milliseconds before others. Theory predicts that scale-free topology allows for synchronization time that does not increase markedly with network size; our experiments with networks of different densities support this prediction. The activity of early-to-fire neurons reliably forecasts an upcoming network spike and provides means for expedited propagation between assemblies. We demonstrate this capacity by observing the dynamics of two artificially coupled assemblies in vitro, using neuronal activity of one as a trigger for electrical stimulation of the other.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c26db4c1fc8e591cb33c83c3ff5add4e094aad05

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In Cellular and Molecular Neuroscience

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Citations in the corpus

Colloquium: Criticality and dynamical scaling in living systems (2017, background): “This generates irregular cascades or outbursts of activity interspersed by quiescent periods, as empirically observed both in vitro (Eytan and Marom, 2006; Sanchez-Vives and McCormick, 2000; Segev and Ben-Jacob, 2001; Segev et al., 2001; Tabak and Latham, 2003) and in vivo (Meister et al., 1991;…”
Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (2015, methodology): “At a smaller scale, the effort of reconstructing networks from neural activity can be traced back to work done by Eytan-Maron in the year 2006 [1].”
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro (2008, result): “Spiking activity from dissociated cultures and organotypic cultures consisted of periods of relative quiescence, punctuated by bursts that involved many neurons, consistent with results reported previously (Segev at al, 2002; Tateno et al., 2004; Van Pelt et al., 2004; Wagenaar et al., 2005; Baker e”
Reliable neuronal logic devices from patterned hippocampal cultures (2008, background): “Activity in cultured neural networks in vitro is characterized by synchronized large-scale network bursts in which practically all of the neurons participate, each firing a number of spike”
Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality (2009, background): “Spatiotemporal correlations of spikes in the visual cortex are similar when the awake animal is simply sitting in darkness or observing natural scenes (Fiser et al., 2004).”