6 papers · 1 filter
Minibatch Processing in Spiking Neural Networks
Daniel J. Saunders, Cooper Sigrist, Kenneth Chaney +2
Spiking neural networks (SNNs) are a promising candidate for biologically-inspired and energy efficient computation. However, their simulation is notoriously time consuming, and ma…
Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data
Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi +2
Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are…
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
Daniel J. Saunders, Devdhar Patel, Hananel Hazan +2
In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by…
STDP Learning of Image Patches with Convolutional Spiking Neural Networks
Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma +1
Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning. A class of \textit{convolutional s…
Unsupervised Learning with Self-Organizing Spiking Neural Networks
Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi +2
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 tra…
BindsNET: A machine learning-oriented spiking neural networks library in Python
Hananel Hazan, Daniel J. Saunders, Hassaan Khan +3
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithm…