Dataset Information

Sparse coding and high-order correlations in fine-scale cortical networks.

ABSTRACT: Connectivity in the cortex is organized at multiple scales, suggesting that scale-dependent correlated activity is particularly important for understanding the behaviour of sensory cortices and their function in stimulus encoding. We analysed the scale-dependent structure of cortical interactions by using maximum entropy models to characterize multiple-tetrode recordings from primary visual cortex of anaesthetized macaque monkeys (Macaca mulatta). We compared the properties of firing patterns among local clusters of neurons (<300 microm apart) with those of neurons separated by larger distances (600-2,500 microm). Here we report that local firing patterns are distinctive: whereas multi-neuronal firing patterns at larger distances can be predicted by pairwise interactions, patterns within local clusters often show evidence of high-order correlations. Surprisingly, these local correlations are flexible and rapidly reorganized by visual input. Although they modestly reduce the amount of information that a cluster conveys, they also modify the format of this information, creating sparser codes by increasing the periods of total quiescence, and concentrating information into briefer periods of common activity. These results imply a hierarchical organization of neuronal correlations: simple pairwise correlations link neurons over scales of tens to hundreds of minicolumns, but on the scale of a few minicolumns, ensembles of neurons form complex subnetworks whose moment-to-moment effective connectivity is dynamically reorganized by the stimulus.

SUBMITTER: Ohiorhenuan IE

PROVIDER: S-EPMC2912961 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Sparse coding and high-order correlations in fine-scale cortical networks.

Ohiorhenuan Ifije E IE Mechler Ferenc F Purpura Keith P KP Schmid Anita M AM Hu Qin Q Victor Jonathan D JD

Nature 20100704 7306

Connectivity in the cortex is organized at multiple scales, suggesting that scale-dependent correlated activity is particularly important for understanding the behaviour of sensory cortices and their function in stimulus encoding. We analysed the scale-dependent structure of cortical interactions by using maximum entropy models to characterize multiple-tetrode recordings from primary visual cortex of anaesthetized macaque monkeys (Macaca mulatta). We compared the properties of firing patterns am ...[more]

PMID: 20601940

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Project description:In searching for genetic variants for complex diseases with deep sequencing data, genomic marker sets of high-dimensional genotypic data and sparse functional variants are quite common. Existing sequence association tests are incapable of identifying such marker sets or individual causal loci, although they appeared powerful to identify small marker sets with dense functional variants. In sequence association studies of admixed individuals, cryptic relatedness and population structure are known to confound the association analyses.We here propose a unified marker wise test (uFineMap) to accurately localize causal loci and a unified high-dimensional set based test (uHDSet) to identify high-dimensional sparse associations in deep sequencing genomic data of multi-ethnic individuals with random relatedness. These two novel tests are based on scaled sparse linear mixed regressions with Lp (0?<?p?<?1) norm regularization. They jointly adjust for cryptic relatedness, population structure and other confounders to prevent false discoveries and improve statistical power for identifying promising individual markers and marker sets that harbor functional genetic variants of a complex trait.With large scale simulation data and real data analyses, the proposed tests appropriately controlled Type I error rates and appeared to be more powerful than several prominent methods. We illustrated their practical utilities by the applications to DNA sequence data of Framingham Heart Study for osteoporosis. The proposed tests identified 11 novel significant genes that were missed by the prominent famSKAT and GEMMA. In particular, four out of six most significant pathways identified by the uHDSet but missed by famSKAT have been reported to be related to BMD or osteoporosis in the literature.The computational toolkit is available for academic use: https://sites.google.com/site/shaolongscode/home/uhdsetwyp@tulane.eduSupplementary data are available at Bioinformatics online.

Dataset Information

Sparse coding and high-order correlations in fine-scale cortical networks.

Publications

Sparse coding and high-order correlations in fine-scale cortical networks.

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