250 citations · 331 across the 7 of their papers we have counts for
8 papers
Compute-Optimal Scaling Laws for Spiking Language Models
Dan Saunders, Benjamin Ren, Catherine He
Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiking language models is uncharacter…
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…
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
Devdhar Patel, Hananel Hazan, Daniel J. Saunders +2
Deep Reinforcement Learning (RL) demonstrates excellent performance on tasks that can be solved by trained policy. It plays a dominant role among cutting-edge machine learning appr…
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…