Connection-type-specific biases make uniform random network models consistent with cortical recordings.
Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a...
Main Authors: | , , , , |
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Format: | Journal article |
Language: | English |
Published: |
American Physiological Society: Journal of Neurophysiology
2014
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