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 | 2002 |

James A. Bednar, Amol Kelkar, and Risto Miikkulainen, Neurocomputing, Vol. 44--46 (2002), pp. 315-321. | |

Scaling Self-Organizing Maps To Model Large Cortical Networks | 2001 |

James A. Bednar, Amol Kelkar, and Risto Miikkulainen, Neuroinformatics (2001), pp. 275-302. |