Comparison of power consumption in optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

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Accuracy of different electrical and optical neural networks for various input noise levels. by Reyhane Ahmadi (14857363)

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Processing results and code. by Lei Wang (6656)

Accuracy of different electrical and optical neural network on Caltech and ETH-80 data sets. by Reyhane Ahmadi (14857363)

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CY45-0.5/1#9 Code file for data processing and prediction results. by Yanhua Xian (21417128)

GA pseudo-code. by Jianpeng Zhang (528185)

Comparison of grayscale conversion algorithms. by Tianke Fang (22648035)

Comparison of noise reduction algorithms. by Tianke Fang (22648035)

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

“…Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. …”

Code and data for the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms" by Noelle Madyson Daigle (22120114)

“…<p dir="ltr">Code and data for analysis detailed in the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms." …”

Performance of algorithms outside the training species. by Sven B. Gould (12237287)

The source code of LazyAct. by Hongjie Zhang (136127)

“…The inferences reduction significantly decreases the time and FLOPs required by the <i>LazyAct</i> algorithm to complete tasks. Code is available here <a href="https://www.dropbox.com/scl/fo/wyoqo6q9gyt86zobfgbvx/h?…”

a. How various statistical models account for modulation classification performance across the entire dataset. by Chris Scholes (3309477)

A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

Pseudo-code for the study design model. by Jianpeng Zhang (528185)

Comparative analysis of spike sorting using Uniform Manifold Approximation and Projection (UMAP) vs. feature-based methods in in vivo extracellular recordings. by Daniel Suárez-Barrera (22676654)

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Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. by Daniel Suárez-Barrera (22676654)

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Spatiotemporal spike sorting on multi-electrode array (MEA) data: Uniform Manifold Approximation and Projection (UMAP) vs. SpyKING CIRCUS (SC). by Daniel Suárez-Barrera (22676654)

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Uniform Manifold Approximation and Projection (UMAP)-based sorting enhances detection of low-firing rate neurons and boosts mutual information. by Daniel Suárez-Barrera (22676654)

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Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. by Daniel Suárez-Barrera (22676654)

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