evt_MNIST: A spike based version of traditional MNIST
This provides a domain-specific benchmark for SNN researchers, but it is incremental as it adapts an existing dataset to a new format.
The authors tackled the lack of spiking input datasets for Spiking Neural Networks (SNN) by creating evt_MNIST, a spike-based version of the MNIST dataset using Poisson distributions to generate irregular spike timings, and demonstrated its Poissonian properties for neural network evaluation.
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.