75 citations · 303 across the 17 of their papers we have counts for
19 papers
Machine learning using magnetic stochastic synapses
Matthew O. A. Ellis, Alex Welbourne, Stephan J. Kyle +4
The impressive performance of artificial neural networks has come at the cost of high energy usage and CO emissions. Unconventional computing architectures, with magnetic syste…
A perspective on physical reservoir computing with nanomagnetic devices
Dan A Allwood, Matthew O A Ellis, David Griffin +11
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this succ…
Reconfigurable Reservoir Computing in a Magnetic Metamaterial
Ian Vidamour, Charles Swindells, Guru Venkat +10
In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting c…
Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics
Ian T Vidamour, Matthew O A Ellis, David Griffin +12
Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for…
EchoVPR: Echo State Networks for Visual Place Recognition
Anil Ozdemir, Mark Scerri, Andrew B. Barron +4
Recognising previously visited locations is an important, but unsolved, task in autonomous navigation. Current visual place recognition (VPR) benchmarks typically challenge models…
A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning
Matthew T. Whelan, Tony J. Prescott, Eleni Vasilaki
Hippocampal reverse replay is thought to contribute to learning, and particularly reinforcement learning, in animals. We present a computational model of learning in the hippocampu…