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Towards a Comprehensive Theory of Reservoir Computing
Denis Kleyko, Christopher J. Kymn, E. Paxon Frady +2
In reservoir computing, an input sequence is processed by a recurrent neural network, the reservoir, which transforms it into a spatial pattern that a shallow readout network can t…
High-resolution spatial memory requires grid-cell-like neural codes
Madison Cotteret, Christopher J. Kymn, Hugh Greatorex +3
Continuous attractor networks (CANs) are widely used to model how the brain temporarily retains continuous behavioural variables via persistent recurrent activity, such as an anima…
A Grid Cell-Inspired Structured Vector Algebra for Cognitive Maps
Sven Krausse, Emre Neftci, Friedrich T. Sommer +1
The entorhinal-hippocampal formation is the mammalian brain's navigation system, encoding both physical and abstract spaces via grid cells. This system is well-studied in neuroscie…
Computing with Residue Numbers in High-Dimensional Representation
Christopher J. Kymn, Denis Kleyko, E. Paxon Frady +4
We introduce Residue Hyperdimensional Computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors. We show ho…