Experimental data indicate that the cerebral cortex is spatially structured. Connections between neurons are not entirely random but rather seem to follow certain rules, leading to well-defined patterns of connectivity. These patterns shape the manner of feedback that each neuron receives and to which it, in turn, contributes. The connectivity thus strongly influences the spatio-temporal dynamics that arise in the cortex and, in particular, the way in which it processes sensory input.
The role of connectivity on sensory processing in the cortex has been studied in theoretical models. However, the general lack of detailed anatomical and electro-physiological data on patterns of connectivity has made a comparison between the models and the actual cerebra l cortex difficult. Only now is sufficient experimental data available to allow for a modelling effort with realistic connectivity. The present project proposes making use of the latest experimental data to build large networks of spiking neurons and study the emergent spatio-temporal dynamics.
It will study, analytically and numerically, both the intrinsic cortical states that arise solely due to feedback, as well as the implications of the connectivity on sensory processing in the visual cortex and spatial working memory in the prefrontal cortex. Special focus will be given to the role of long-range excitatory connections, which form geometric patterns along the surface of the cortex.
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