
Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware
This article reviews recent progress in the development of the computing...
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Perceptron Theory for Predicting the Accuracy of Neural Networks
Many neural network models have been successful at classification proble...
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Cellular Automata Can Reduce Memory Requirements of CollectiveState Computing
Various nonclassical approaches of distributed information processing, ...
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Variable Binding for Sparse Distributed Representations: Theory and Applications
Symbolic reasoning and neural networks are often considered incompatible...
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Resonator networks for factoring distributed representations of data structures
The ability to encode and manipulate data structures with distributed ne...
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Neuromorphic NearestNeighbor Search Using Intel's Pohoiki Springs
Neuromorphic computing applies insights from neuroscience to uncover inn...
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Density Encoding Enables ResourceEfficient Randomly Connected Neural Networks
The deployment of machine learning algorithms on resourceconstrained ed...
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Resonator Circuits for factoring highdimensional vectors
We describe a type of neural network, called a Resonator Circuit, that f...
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Robust computation with rhythmic spike patterns
Information coding by precise timing of spikes can be faster and more en...
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A theory of sequence indexing and working memory in recurrent neural networks
To accommodate structured approaches of neural computation, we propose a...
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Theory of the superposition principle for randomized connectionist representations in neural networks
To understand cognitive reasoning in the brain, it has been proposed tha...
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E. Paxon Frady
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