Search Results for author: Luke Taylor

Found 3 papers, 2 papers with code

Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons

1 code implementation NeurIPS 2023 Luke Taylor, Andrew J King, Nicol S Harper

The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational neuroscience and has been instrumental in studying our brains $\textit{in silico}$.

Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks

1 code implementation30 May 2022 Luke Taylor, Andrew King, Nicol Harper

Spiking neural networks (SNNs), particularly the single-spike variant in which neurons spike at most once, are considerably more energy efficient than standard artificial neural networks (ANNs).

Audio Classification Image Classification +3

Improving Deep Learning using Generic Data Augmentation

no code implementations20 Aug 2017 Luke Taylor, Geoff Nitschke

Deep artificial neural networks require a large corpus of training data in order to effectively learn, where collection of such training data is often expensive and laborious.

Data Augmentation

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