A self-organizing model of spiking neurons with dynamic thresholds and lateral excitatory and inhibitory connections is presented and tested in the image segmentation task. The model integrates two previously separate lines of research in modeling the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures and lateral connections could 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 activity. Although these approaches differ in how they model the neuron, they have 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 a network, thus presenting a unified model of development and functional dynamics in the primary visual cortex.

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In Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), 1120-1125, Nagoya, Japan, 1997. San Francisco: Kaufmann.

@InProceedings{choe:ijcai97, title={Self-Organization And Segmentation With Laterally Connected Spiking Neurons}, author={Yoonsuck Choe and Risto Miikkulainen}, booktitle={Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97)}, address={Nagoya, Japan}, publisher={San Francisco: Kaufmann}, key={IJCAI}, pages={1120-1125}, url="http://nn.cs.utexas.edu/?choe:ijcai97", year={1997} }