  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.
