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05/26/2023
Efficient Decoding of Compositional Structure in Holistic Representations
We investigate the task of retrieving information from compositional dis...
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03/23/2023
Learning and generalization of compositional representations of visual scenes
Complex visual scenes that are composed of multiple objects, each with a...
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12/07/2022
Efficient Optimization with Higher-Order Ising Machines
A prominent approach to solving combinatorial optimization problems on p...
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04/05/2022
Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks
An open problem in neuroscience is to explain the functional role of osc...
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04/01/2022
Deep Learning in Spiking Phasor Neural Networks
Spiking Neural Networks (SNNs) have attracted the attention of the deep ...
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03/02/2022
Integer Factorization with Compositional Distributed Representations
In this paper, we present an approach to integer factorization using dis...
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01/26/2022
A probabilistic latent variable model for detecting structure in binary data
We introduce a novel, probabilistic binary latent variable model to dete...
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01/24/2022
Neural Manifold Clustering and Embedding
Given a union of non-linear manifolds, non-linear subspace clustering or...
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11/05/2021
Efficient Neuromorphic Signal Processing with Loihi 2
The biologically inspired spiking neurons used in neuromorphic computing...
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09/08/2021
Computing on Functions Using Randomized Vector Representations
Vector space models for symbolic processing that encode symbols by rando...
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06/09/2021
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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12/14/2020
Perceptron Theory for Predicting the Accuracy of Neural Networks
Many neural network models have been successful at classification proble...
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10/07/2020
A Neural Network MCMC sampler that maximizes Proposal Entropy
Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probabi...
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10/07/2020
Cellular Automata Can Reduce Memory Requirements of Collective-State Computing
Various non-classical approaches of distributed information processing, ...
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09/14/2020
Variable Binding for Sparse Distributed Representations: Theory and Applications
Symbolic reasoning and neural networks are often considered incompatible...
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07/07/2020
Resonator networks for factoring distributed representations of data structures
The ability to encode and manipulate data structures with distributed ne...
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05/06/2020
A Model for Image Segmentation in Retina
While traditional feed-forward filter models can reproduce the rate resp...
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05/04/2020
Complex Amplitude-Phase Boltzmann Machines
We extend the framework of Boltzmann machines to a network of complex-va...
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04/27/2020
Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs
Neuromorphic computing applies insights from neuroscience to uncover inn...
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10/17/2019
Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces
Energy-Based Models (EBMs) outputs unmormalized log-probability values g...
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06/19/2019
Resonator Circuits for factoring high-dimensional vectors
We describe a type of neural network, called a Resonator Circuit, that f...
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01/23/2019
Robust computation with rhythmic spike patterns
Information coding by precise timing of spikes can be faster and more en...
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02/28/2018
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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07/05/2017