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要旨:
Most current computational models of neocortical networks assume a homogeneous and isotropic arrangement of local synaptic couplings between neurons. Sparse, recurrent connectivity is typically implemented with simple statistical wiring rules. For spatially extended networks, however, such random graph models are inadequate because they ignore the traits of neuron geometry, most notably various distance dependent features of horizontal connectivity. It is to be expected that such non-random structural attributes have a great impact, both on the spatio-temporal activity dynamics and on the biological function of neocortical networks. Here we review the neuroanatomical literature describing long-range horizontal connectivity in the neocortex over distances of up to eight millimeters, in various cortical areas and mammalian species. We extract the main common features from these data to allow for improved models of large-scale cortical networks. Such models include, next to short-range neighborhood coupling, also long-range patchy connections.
We show that despite the large variability in published neuroanatomical data it is reasonable to design a generic model which generalizes over different cortical areas and mammalian species. Later on, we critically discuss this generalization, and we describe some examples of how to specify the model in order to adapt it to specific properties of particular cortical areas or species.
