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Neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back. (English) Zbl 1119.92007

Summary: The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study [T. Kenet et al., Nature 425, 954–956 (2003)] found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene.

MSC:

92C20 Neural biology
92B20 Neural networks for/in biological studies, artificial life and related topics
92C05 Biophysics
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