Temporal Pattern Classification with Spiking Neural Networks

Abstract

References [1] A. Rowbottom, “Temporal Pattern Classification with Spiking Neural Networks”, 2015, DOI: http://dx.doi.org/10.6084/m9.figshare.1536281 [2] Pfister, J., Toyoizumi, T., Barber, D., & Gerstner, W. (2006). Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Computation, 18(6), 1318-1348. [3] R. Gutig and H. Sompolinsky, “The tempotron: a neuron that learns spike timing-based decisions.,” 2006. [4] B. Gardner and A. Gruning, “Classifying Patterns in a Spiking Neural,” no. April, pp. 23–25, 2014. We use a single output neuron for Pfister learning, where we match the output spike train with the closest generated target train for each class, using the Van Rossum distance metric. These target spike trains can have multiple spikes, and part of this research investigates how the number of spikes affects the learning.