Self-Organization And Segmentation In A Laterally Connected Orientation Map Of Spiking Neurons (1998)
The RF-SLISSOM model integrates two separate lines of research on computational modeling of the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures such as orientation columns and patterned lateral connections can simultaneously self-organize through input-driven Hebbian adaptation. Spiking neurons with leaky integrator synapses have been used to model image segmentation and binding by synchronization and desynchronization of neuronal group activity. Although these approaches differ in how they model the neuron and what they explain, they share the same overall layout of a laterally connected two-dimensional network. This paper shows how both self-organization and segmentation can be achieved in such an integrated network, thus presenting a unified model of development and functional dynamics in the primary visual cortex.
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Citation:
Neurocomputing:139-157, 1998.
Bibtex:

Yoonsuck Choe Ph.D. Alumni choe [at] tamu edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
LISSOM

The LISSOM package contains the C++, Python, and Scheme source code and examples for training and testing firing-rate...

2001