English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Understanding the coordination structure of neurons in neuronal networks isessential for unraveling the distributed information processing mechanisms inbrain networks. Recent advancements in measurement techniques have resulted inan increasing amount of data on neural activities recorded in parallel,revealing largely heterogeneous correlation patterns across neurons. Yet, themechanistic origin of this heterogeneity is largely unknown because existingtheoretical approaches linking structure and dynamics in neural circuits aremostly restricted to average connection patterns. Here we present a systematicinclusion of variability in network connectivity via tools from statisticalphysics of disordered systems. We study networks of spiking leakyintegrate-and-fire neurons and employ mean-field and linear-response methods tomap the spiking networks to linear rate models with an equivalentneuron-resolved correlation structure. The latter models can be formulated in afield-theoretic language that allows using disorder-average and replicatechniques to systematically derive quantitatively matching beyond-mean-fieldpredictions for the mean and variance of cross-covariances as functions of theaverage and variability of connection patterns. We show that heterogeneity incovariances is not a result of variability in single-neuron firing statisticsbut stems from the sparse realization and variable strength of connections, asubiquitously observed in brain networks. Average correlations between neuronsare found to be insensitive to the level of heterogeneity, which in contrastmodulates the variability of covariances across many orders of magnitude,giving rise to an efficient tuning of the complexity of coordination patternsin neuronal circuits.

Beyond-mean-field theory for the statistics of neural coordination