Modeling self-organization in the visual cortex

To gain insight into biological knowledge organization and development, the Self-Organizing Map architecture can be extended with anatomical receptive fields, lateral connections, Hebbian adaptation, and spiking neurons. The resulting RF-SLISSOM model shows how the observed receptive fields, columnar organization, and-lateral connectivity in the visual cortex can arise through input-driven self-organization, and how such plasticity can account for partial recovery following retinal and cortical lesions. The self-organized network forms a redundancy-reduced sparse coding of the input, which allows it to process massive amounts of information efficiently, and explains how various low-level visual phenomena such as tilt aftereffects and segmentation and binding can emerge. Such models allow understanding biological processes at a very detailed computational level, and are likely to play a major role in cognitive neuroscience in the future.