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evt_MNIST: A spike based version of traditional MNIST an event-based MNIST

Abstract

Benchmarks and datasets have important role in evaluation of machine learning algorithms and neural network implementations. Traditional dataset for images such as MNIST is applied to evaluate efficiency of different training algorithms in neural networks. This demand is different in Spiking Neural Networks (SNN) as they require spiking inputs. It is widely believed, in the biological cortex the timing of spikes is irregular. Poisson distributions provide adequate descriptions of the irregularity in generating appropriate spikes. Here, we introduce a spike-based version of MNSIT (handwritten digits dataset), using Poisson distribution and show the Poissonian property of the generated streams. We introduce a new version of evt_MNIST which can be used for neural network evaluation.
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Dates and versions

hal-01382631 , version 1 (17-10-2016)

Identifiers

  • HAL Id : hal-01382631 , version 1

Cite

Mazdak Fatahi, Mahyar Shahsavari, Mahmood Ahmadi, Arash Ahmadi, Philippe Devienne. evt_MNIST: A spike based version of traditional MNIST an event-based MNIST. 1st International Conference on New Research Achievements in Electrical and Computer Engineering, May 2016, Teheran, Iran. ⟨hal-01382631⟩
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