9 citations · 19 across the 5 of their papers we have counts for
6 papers · 1 filter
Deep Learning in Spiking Phasor Neural Networks
Connor Bybee, E. Paxon Frady, Friedrich T. Sommer
Spiking Neural Networks (SNNs) have attracted the attention of the deep learning community for use in low-latency, low-power neuromorphic hardware, as well as models for understand…
Variable Binding for Sparse Distributed Representations: Theory and Applications
E. Paxon Frady, Denis Kleyko, Friedrich T. Sommer
Symbolic reasoning and neural networks are often considered incompatible approaches. Connectionist models known as Vector Symbolic Architectures (VSAs) can potentially bridge this…
Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs
E. Paxon Frady, Garrick Orchard, David Florey +7
Neuromorphic computing applies insights from neuroscience to uncover innovations in computing technology. In the brain, billions of interconnected neurons perform rapid computation…
Robust computation with rhythmic spike patterns
E. Paxon Frady, Friedrich T. Sommer
Information coding by precise timing of spikes can be faster and more energy-efficient than traditional rate coding. However, spike-timing codes are often brittle, which has limite…
A theory of sequence indexing and working memory in recurrent neural networks
E. Paxon Frady, Denis Kleyko, Friedrich T. Sommer
To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. Th…
Theory of the superposition principle for randomized connectionist representations in neural networks
E. Paxon Frady, Denis Kleyko, Friedrich T. Sommer
To understand cognitive reasoning in the brain, it has been proposed that symbols and compositions of symbols are represented by activity patterns (vectors) in a large population o…