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- W2177896537 abstract "I calculate the mean pairwise correlation between neurons in a network model characterized by strong and random interactions. The mean is calculated with respect to both temporal fluctuations and the fixed spatial disorder of the network interactions. To mimick the topology of the cerebral cortex, the network is constructed with local (recurrent) inhibitory connections and distal (feed-forward) excitatory afferents. The network is fully connected, and both excitatory and inhibitory interactions are strong, of magnitude N − 1 2, where N is the number of neurons in the network, therefore each neuron receives excitatory and inhibitory signals of magnitude N 1 2. In spite of the dense and strong connectivity, I found that the mean correlation is small. The inhibitory feedback strongly reduces the input correlation, and the output correlation is positive and of order N − 1 2. The result agrees with numerical studies, and also with anatomical and physiological measurements in the cortex: a cortical neuron receives about N ≃ 10 4 connections, and the measured pairwise correlations are of order 10 −2 ." @default.
- W2177896537 created "2016-06-24" @default.
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- W2177896537 date "2011-07-15" @default.
- W2177896537 modified "2023-09-27" @default.
- W2177896537 title "Decorrelation by recurrent inhibition in randomly and strongly coupled networks" @default.
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