1 citations · 2 across the 3 of their papers we have counts for
6 papers
Random Feedback Alignment Algorithms to train Neural Networks: Why do they Align?
Dominique Chu, Florian Bacho
Feedback alignment algorithms are an alternative to backpropagation to train neural networks, whereby some of the partial derivatives that are required to compute the gradient are…
Integrate-and-Fire Neurons for Low-Powered Pattern Recognition
Florian Bacho, Dominique Chu
Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the da…
Constraints on Hebbian and STDP learned weights of a spiking neuron
Dominique Chu, Huy Le Nguyen
We analyse mathematically the constraints on weights resulting from Hebbian and STDP learning rules applied to a spiking neuron with weight normalisation. In the case of pure Hebbi…
A thermodynamically consistent chemical spiking neuron capable of autonomous Hebbian learning
Jakub Fil, Dominique Chu
We propose a fully autonomous, thermodynamically consistent set of chemical reactions that implements a spiking neuron. This chemical neuron is able to learn input patterns in a He…
Minimal spiking neuron for solving multi-label classification tasks
Jakub Fil, Dominique Chu
The Multi-Spike Tempotron (MST) is a powerful single spiking neuron model that can solve complex supervised classification tasks. While powerful, it is also internally complex, com…
A thermodynamically consistent model of finite state machines
Dominique Chu, Richard Spinney
Finite state machines (FSMs) are a theoretically and practically important model of computation. We propose a general, thermodynamically consistent model of FSMs and characterise t…