
Computing on Functions Using Randomized Vector Representations
Vector space models for symbolic processing that encode symbols by rando...
read it

Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware
This article reviews recent progress in the development of the computing...
read it

Perceptron Theory for Predicting the Accuracy of Neural Networks
Many neural network models have been successful at classification proble...
read it

A Neural Network MCMC sampler that maximizes Proposal Entropy
Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probabi...
read it

Cellular Automata Can Reduce Memory Requirements of CollectiveState Computing
Various nonclassical approaches of distributed information processing, ...
read it

Variable Binding for Sparse Distributed Representations: Theory and Applications
Symbolic reasoning and neural networks are often considered incompatible...
read it

Resonator networks for factoring distributed representations of data structures
The ability to encode and manipulate data structures with distributed ne...
read it

A Model for Image Segmentation in Retina
While traditional feedforward filter models can reproduce the rate resp...
read it

Complex AmplitudePhase Boltzmann Machines
We extend the framework of Boltzmann machines to a network of complexva...
read it

Neuromorphic NearestNeighbor Search Using Intel's Pohoiki Springs
Neuromorphic computing applies insights from neuroscience to uncover inn...
read it

Annealed Denoising Score Matching: Learning EnergyBased Models in HighDimensional Spaces
EnergyBased Models (EBMs) outputs unmormalized logprobability values g...
read it

Resonator Circuits for factoring highdimensional vectors
We describe a type of neural network, called a Resonator Circuit, that f...
read it

Robust computation with rhythmic spike patterns
Information coding by precise timing of spikes can be faster and more en...
read it

A theory of sequence indexing and working memory in recurrent neural networks
To accommodate structured approaches of neural computation, we propose a...
read it

Theory of the superposition principle for randomized connectionist representations in neural networks
To understand cognitive reasoning in the brain, it has been proposed tha...
read it
Friedrich T. Sommer
is this you? claim profile