Publications

Unsupervised Learning with Self-Organizing Spiking Neural Networks

Published in IJCNN 2018, 2018

We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks.

Recommended citation: Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklos Ruszinko (2018b) https://djsaunde.github.io/files/unsupervised-learning-organizing.pdf

STDP Learning of Image Patches with Convolutional Spiking Neural Networks

Published in IJCNN 2018, 2018

Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning. A class of convolutional spiking neural networks is introduced, trained to detect image features with an unsupervised, competitive learning mechanism. Image features can be shared within subpopulations of neurons, or each may evolve independently to capture different features in different regions of input space. We analyze the time and memory requirements of learning with and operating such networks. The MNIST dataset is used as an experimental testbed, and comparisons are made between the performance and convergence speed of a baseline spiking neural network.

Recommended citation: Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklos Ruszinko (2018a) https://djsaunde.github.io/files/stdp-learning-image-patches.pdf