![]() ![]() At the heart of our solution is the design of a $O \left (\frac\right)$ auxiliary neurons, which is Randomization allows us to solve this task with a very compact network, using ![]() Setting, given two $n$-length patterns of firing neurons, we wish toÄistinguish if the patterns are equal or $\epsilon$-far from equal. Neural algorithms for similarity testing and compression. Recent work, we explored how this stochasticity could be leveraged to solve the It is widely accepted that neural computation is inherently stochastic. ![]() Randomness in efficient neural computation. We focus on tradeoffsÄ«etween computation time and network complexity, along with the role of Inspired model for stochastic spiking neural networks. Download a PDF of the paper titled Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks, by Nancy Lynch and 2 other authors Download PDF Abstract: We study distributed algorithms implemented in a simplified biologically ![]()
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