Abstract
We present a nonlinear, recurrent neural network model of the primary visual cortex with separate ON/OFF-pathways and modifiable afferent as well as intracortical synaptic couplings. Orientation maps emerge driven by random input stimuli. Lateral coupling structures self-organize into DOG profiles under the influence of pronounced emerging cortical activity blobs. The model’s architecture and features are, compared with former models, well justified neurobiologically.
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Burger, T., Lang, E.W. (1999). A network model for the emergence of orientation maps and local lateral circuits. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098199
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DOI: https://doi.org/10.1007/BFb0098199
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