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. 2015 Jul 9;9:222. doi: 10.3389/fnins.2015.00222

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Copyright © 2015 Stromatias, Neil, Pfeiffer, Galluppi, Furber and Liu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Effect of Gaussian weight variance on the performance of spiking DBNs. (A) Receptive fields of 6 representative neurons in the first hidden layer after perturbation with Gaussian weight variance of different CVs. (B) Impact of Gaussian weight variance on classification accuracy. The performance is plotted as a function of input noise levels for two different input rates and different weight distribution CVs. Despite the high weight variance, the performance stays high and remains robust to input noise. All weights are set by 5 bit DAC synapses (one bit is the sign bit). Results over 4 trials.