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Computing with Residue Numbers in High-Dimensional Representation
We introduce Residue Hyperdimensional Computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors.
Efficient Decoding of Compositional Structure in Holistic Representations
In particular, we find that the decoding techniques from the sparse coding and compressed sensing literature (rarely used for Hyperdimensional Computing/Vector Symbolic Architectures) are also well-suited for decoding information from the compositional distributed representations.
Efficient Optimization with Higher-Order Ising Machines
A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i. e., hardware implementations of networks of interacting binary spin variables.
Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks
We show that for values of $Q$ which are the same as the ratio of $\gamma$ to $\theta$ oscillations observed in the hippocampus and the cortex, the associative memory achieves greater capacity and information storage than previous models.
Deep Learning in Spiking Phasor Neural Networks
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 understanding neuroscience.
Integer Factorization with Compositional Distributed Representations
In this paper, we present an approach to integer factorization using distributed representations formed with Vector Symbolic Architectures.
