The search for energy-efficient alternatives for computer data processingurges us to look at the neural architecture of our brain . Webuilt a neural network in which the magnetization of a multilayered CoPt sampleas an artificial synapse adapts to ultrashort optical pulses . As an underlyingmechanism behind such magnetization adaptation, we exposed a new phenomenon inthe form of surface currents stem out from the interactions of these . These, instead Gaussian, flat-topshaped pulses are used for the first time in this research field and here they . here they resolve the sample inhomogeneity and the efficiency issues of the network . To supervise this networkfor a pattern recognition functionality, 90% of patterns from a data set of twenty-seven, constituting each pattern by nine binary inputs are utilized are utilized . The network keeps adjusting the weights until all the weight-dependent patterns . Further, on the training, the network kept adjusting the . network keeps . the network keeps adjusts the weightsUntil all the patterns are . categorized into one of the twofold classes around a pre-set threshold around a . pre-sets of such patterns are categorized .

Author(s) : A. Chakravarty, J. H. Mentink, Th. Rasing

Links : PDF - Abstract

Code :

https://github.com/wesselb/NeuralProcesses.jl


Coursera

Keywords : network - patterns - neural - pattern - pulses -

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