evt_MNIST: A spike based version of traditional MNIST

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...

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Bibliographic Details
Published in:arXiv.org 2016-04
Main Authors: Fatahi, Mazdak, Ahmadi, Mahmood, Shahsavari, Mahyar, Ahmadi, Arash, Devienne, Philippe
Format: Article
Language:English
Subjects:
Algorithms
Digits
Handwriting
Machine learning
Neural networks
Poisson distribution
Spikes
Spiking
Online Access:Get full text
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Summary: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.
ISSN:2331-8422