GLISSOM: Modeling Large Cortical Maps

Densely-connected self-organizing models of the cortex can be quite computationally intensive to simulate. We are working on two methods for making such simulations more practical. First, we have derived a set of scaling equations that allows small networks to be used as approximations for larger ones, while allowing the same parameters to be used for full-scale simulations once the concept has been demonstrated. Second, we are investigating how these scaling equations can be applied to a network as it is organizing, in order to develop a large, detailed final network in much less time (and using much less memory) than would otherwise be required. This growing laterally-interconnected self-organizing map algorithm is based on RF-LISSOM and is called GLISSOM.

Modeling Large Cortical Networks With Growing Self-Organizing Maps | James A. Bednar, Amol Kelkar, and Risto Miikkulainen | In Computational Neuroscience, 44--46, 315-321, 2002. | 2002 |

Scaling Self-Organizing Maps To Model Large Cortical Networks | James A. Bednar, Amol Kelkar, and Risto Miikkulainen | Neuroinformatics:275-302, 2001. | 2001 |