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Posted by vklin on 15 Apr 2014 at 07:56 GMT
The information flow in neural circuits and their computational capabilities strongly depend on the structure and strength of connections between neurons, hence various efforts have been made to clarify the organization of neuronal wiring in the brain – a well-known example among such efforts may be “connectonome”. Though the reconstruction of full circuit diagrams is still a distant goal even for local circuits, recent experimental data shows that cortical networks are far from random even on local scales. In the present study, we derive a model that consistently explains the non-random features of local cortical circuits. Unexpectedly, this model predicts that these circuits contain groups of excitatory neurons, the so-called 'clusters', which are almost all-to-all connected. Such densely connected structures may be counter-intuitive given the average sparse connectivity of cortical circuits. Nevertheless, this simple model consistently accounts for many physiological connectivity data reported for cortical network motifs. Moreover, a large-scale network model embedding many such clusters demonstrates persistent neuronal firing of co-activated clusters, yielding a limited memory capacity as observed in working memory tasks. Our results will inform experiments on the precise structure of cortical neuronal wiring and theoretical attempts to build realistic cortical circuit models.