A Small World of Neuronal Synchrony

By Shan Yu, Debin Huang, Wolf Singer, Danko Nikolic. Cerebral Cortex, 2008

336 citations recorded by the source corpus.

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of “hubs” in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/061f4fd2eba0f2a32034d4d58b816752ec1adb11

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In Cognitive Neuroscience

Citations in the corpus

Are Biological Systems Poised at Criticality? (2010, background): “Ising models also proved useful for studying neural activity in the visual cortex of cats [44] and macaque monkeys [45, 46].”
Spin glass models for a network of real neurons (2009, result): “After the initial success in the salamander retina, similarly encouraging results were obtained in the primate retina, under very diﬀerent stimulus conditions [8, 9], in visual cortex [10, 11], and in networks grown in vitro [12].”
Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data (2009, neutral): “Neuronal synchrony in the cat visual cortex has also been reported to exhibit small-world characteristics using an Ising model (Yu, et al. 2008).”
Structured Connectivity in Cerebellar Inhibitory Networks (2014, background): “Zhao et al. (2011) showed that deviations from random networks caused by overrepresenting different network motifs involving two connections in either a divergent, convergent, or chain configuration can have opposing effects on synchrony.”
Brain graphs: graphical models of the human brain connectome. (2011, methodology): “For graphical analysis of neurophysiological data on electromagnetic fields, including microelectrode recordings from cortical tissue (Yu et al. 2008) as well as scalp electrode recordings in EEG or surface sensors in MEG, it may be reasonable to define each electrode or signal sensor as a node.”
A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks (2011, background): “Small-world topology has also been identified at the cellular-network scale in functional cortical neuronal circuits in mammals (15, 16) and even in the nervous system of the nematode Caenorhabditis elegans (8).”
Being Critical of Criticality in the Brain (2012, methodology): “…applying the Ising model to neuronal activity patterns found in actual data (Schneidman et al., 2006; Shlens et al., 2006, 2009; Tang et al., 2008; Yu et al., 2008, 2011; Tkacik et al., 2009; Yeh et al., 2010) you are entirely right to say that the Ising model is far too simple to completely…”