piriform (olfactory) cortex model on the hypercube
We present a concurrent hypercube implementation of a neurophysiological model for the piriform (olfactory) cortex. The project was undertaken as the first step towards constructing a general neural network simulator on the hypercube, suitable both for applied and biological nets. The method presented here is expected to be useful for a class of complex and computationally expensive network models with long range connectivity and non-homogeneous activity patterns. The hypercube communication for the fully interconnected case is efficiently realized by the fold algorithm, constructed previously for problems in concurrent matrix algebra whereas the patchy activity is successfully load balanced by the scattered decomposition. We discuss also briefly other communication strategies, relevant for sparse and variable connectivities. Sample numerical results presented here were derived on the NCUBE hypercube at Caltech.
model hypercube concurrency implementation project general neural networks simulation method class complexity network model connection activation pattern communication case algorithm algebra decomposition strategies relevance variability