Self-organization and segmentation in a laterally connected orientation map of spiking neurons

The RF-SLISSOM model integrates two separate lines of research on computati onal modeling of the visual cortex. Laterally connected self-organizing map s have been used to model how afferent structures such as orientation colum ns and patterned lateral connections can simultaneously self-organize throu gh input-driven Hebbian adaptation. Spiking neurons with leaky integrator s ynapses have been used to model image segmentation and binding by synchroni zation and desynchronization of neuronal group activity. Although these app roaches differ in how they model the neuron and what they explain, they sha re the same overall layout of a laterally connected two-dimensional network . This paper shows how both self-organization and segmentation can be achie ved in such an integrated network, thus presenting a unified model of devel opment and functional dynamics in the primary visual cortex. (C) 1998 Elsev ier Science B.V. All rights reserved.