In this thesis a model of the primary visual cortex (V1) is presented. The centerpiece of this model is an abstract hypercolumn model, derived from the Bayesian Confidence Propagation Neural Network (BCPNN). This model functions as a building block of the proposed laminar V1 model, which consists of layer 4 and 2/3 components.
The V1 model is developed during exposure to visual input using the BCPNN incremental learning rule. The connectivity pattern demonstrated by this correlation-based network model is similar to that of V1. In both modeled cortical layers local horizontal connections are dense, whereas long-range horizontal connections are sparse. Layer 4 local horizontal connections are biased towards the iso-orientation domain, whereas long-range horizontal connections are equally distributed between all orientation domains. In contrast, both local and long-range horizontal connections of the layer 2/3 are biased towards the iso-orientation domains. The layer 2/3 network is axially specific as well. Thus, this V1 model demonstrates how the recurrent connections can be self-organized and generate a cortex like connectivity pattern.
Furthermore, in both layers inhibition operates within a modeled hypercolumn. This is in line with what is found in the V1, i.e. inhibition is mainly local, whereas excitation extends far beyond the inhibitory network. Observe also that neither excitation nor inhibition dominates the network.
Based on this connectivity pattern the V1 model addresses several response properties of the neurons, such as orientation selectivity, contrast-invariance of orientation tuning, response saturation followed by normalization, cross-orientation inhibition. Configuration-specific facilitation phenomena are explained by the axially specific layer 2/3 long-range horizontal connections. It is hypothesized that spike and burst synchronization might aid this process.
The main conclusion drawn is that it is possible to explain connectivity as well as several response properties of the neurons by a general V1 model, which is faithful to the known anatomy and physiology of the neocortex. Thus, when simplicity is combined with biological plausibility the models can give valuable insight into structure and function of cortical circuitry.
Keywords: primary visual cortex, hypercolumn, cortical microcircuit, attractor network, recurrent artificial neural network, Bayesian confidence propagation neural network, developmental models, intracortical connections, long-range horizontal connections, orientation selectivity, response saturation, normalization, contrast-invariance of orientation selectivity, configuration-specific facilitation, summation pools
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