250 citations · 352 across the 16 of their papers we have counts for
9 papers · 1 filter
Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
Alexander Yeung, Peter DelMastro, Arjun Karuvally +3
Reservoir Computing is a machine learning approach that uses the rich repertoire of complex system dynamics for function approximation. Current approaches to reservoir computing us…
Transient Dynamics in Lattices of Differentiating Ring Oscillators
Peter DelMastro, Arjun Karuvally, Hananel Hazan +2
Recurrent neural networks (RNNs) are machine learning models widely used for learning temporal relationships. Current state-of-the-art RNNs use integrating or spiking neurons -- tw…
Heuristically Adaptive Diffusion-Model Evolutionary Strategy
Benedikt Hartl, Yanbo Zhang, Hananel Hazan +1
Diffusion Models represent a significant advancement in generative modeling, employing a dual-phase process that first degrades domain-specific information via Gaussian noise and r…
Diffusion Models are Evolutionary Algorithms
Yanbo Zhang, Benedikt Hartl, Hananel Hazan +1
In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolutio…
Memory via Temporal Delays in weightless Spiking Neural Network
Hananel Hazan, Simon Caby, Christopher Earl +2
A common view in the neuroscience community is that memory is encoded in the connection strength between neurons. This perception led artificial neural network models to focus on c…
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…
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…