people:taylor_berger:infinite_brain_paper
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An Indefinitely Scalable Brain: Implicit Neural Networks in a Spatially Distributed System
Abstract: We define an implicit, spatially distributed neural network and show pattern recognition is not only viable, but can be reconstructed in volatile systems. We use the Moveable Feast Machine architecture to investigate a neural network with implied connections between neurons based on their proximity. We show that this type of neural network can be scaled indefinitely and learns arbitrary patterns despite adverse learning conditions. We also found the most important factor for successful pattern recognition is the density of neurons.
people/taylor_berger/infinite_brain_paper.1413126206.txt.gz · Last modified: 2014/10/12 15:03 by tberge01