3 accepted papers
Spike train data find a growing list of applications in computational neuroscience, streaming data and finance. Statistical analysis of spike trains is based on various probabilistic and neural network models. The statistical approach relies on parametric or nonparametric specifications of the under…
This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth Mover's Distance (EMD) between spike trains with spiking neural networks (SNN). We train this model on images of the MNIST dataset converted…
We investigate the use of integrate-and-fire systems to efficiently sample classes of non-bandlimited signals such as bursts of spikes. The sampling in this case is based on storing some timing information about the signal, and no information about its amplitude. We demonstrate that perfect reconstr…