Neural Comput - Anatomical constraints on lateral competition in columnar cortical architectures.

Tópicos

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Resumo

Competition is a well-studied and powerful mechanism for information processing in neuronal networks, providing noise rejection, signal restoration, decision making and associative memory properties, with relatively simple requirements for network architecture. Models based on competitive interactions have been used to describe the shaping of functional properties in visual cortex, as well as the development of functional maps in columnar cortex. These models require competition within a cortical area to occur on a wider spatial scale than cooperation, usually implemented by lateral inhibitory connections having a longer range than local excitatory connections. However, measurements of cortical anatomy reveal that the spatial extent of inhibition is in fact more restricted than that of excitation. Relatively few models reflect this, and it is unknown whether lateral competition can occur in cortical-like networks that have a realistic spatial relationship between excitation and inhibition. Here we analyze simple models for cortical columns and perform simulations of larger models to show how the spatial scales of excitation and inhibition can interact to produce competition through disynaptic inhibition. Our findings give strong support to the direct coupling effect-that the presence of competition across the cortical surface is predicted well by the anatomy of direct excitatory and inhibitory coupling and that multisynaptic network effects are negligible. This implies that for networks with short-range inhibition and longer-range excitation, the spatial extent of competition is even narrower than the range of inhibitory connections. Our results suggest the presence of network mechanisms that focus on intra-rather than intercolumn competition in neocortex, highlighting the need for both new models and direct experimental characterizations of lateral inhibition and competition in columnar cortex.

Resumo Limpo

competit wellstudi power mechan inform process neuron network provid nois reject signal restor decis make associ memori properti relat simpl requir network architectur model base competit interact use describ shape function properti visual cortex well develop function map columnar cortex model requir competit within cortic area occur wider spatial scale cooper usual implement later inhibitori connect longer rang local excitatori connect howev measur cortic anatomi reveal spatial extent inhibit fact restrict excit relat model reflect unknown whether later competit can occur corticallik network realist spatial relationship excit inhibit analyz simpl model cortic column perform simul larger model show spatial scale excit inhibit can interact produc competit disynapt inhibit find give strong support direct coupl effectthat presenc competit across cortic surfac predict well anatomi direct excitatori inhibitori coupl multisynapt network effect neglig impli network shortrang inhibit longerrang excit spatial extent competit even narrow rang inhibitori connect result suggest presenc network mechan focus intrarath intercolumn competit neocortex highlight need new model direct experiment character later inhibit competit columnar cortex

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