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Your search keyword '"Benjamin Moseley"' showing total 309 results
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309 results on '"Benjamin Moseley"'

1. A comparison of respiratory particle emission rates at rest and while speaking or exercising

Author

Christopher M. Orton, Henry E. Symons, Benjamin Moseley, Justice Archer, Natalie A. Watson, Keir E. J. Philip, Sadiyah Sheikh, Brian Saccente-Kennedy, Declan Costello, William J. Browne, James D. Calder, Bryan R. Bzdek, James H. Hull, Jonathan P. Reid, and Pallav L. Shah

Subjects
Medicine
Abstract

Orton and Symons et al. compare respiratory particle sizes and emission rates by sampling exhalates from participants at rest, and while speaking or exercising. They find that vocalisation produces larger particles and that while emission rates are similar between speaking and vigorous exercise, very vigorous exercise leads to higher rates.

Published
2022
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2. Pre-Synaptic Pool Modification (PSPM): A supervised learning procedure for recurrent spiking neural networks.

Author

Bryce Allen Bagley, Blake Bordelon, Benjamin Moseley, and Ralf Wessel

Subjects
Medicine, Science
Abstract

Learning synaptic weights of spiking neural network (SNN) models that can reproduce target spike trains from provided neural firing data is a central problem in computational neuroscience and spike-based computing. The discovery of the optimal weight values can be posed as a supervised learning task wherein the weights of the model network are chosen to maximize the similarity between the target spike trains and the model outputs. It is still largely unknown whether optimizing spike train similarity of highly recurrent SNNs produces weight matrices similar to those of the ground truth model. To this end, we propose flexible heuristic supervised learning rules, termed Pre-Synaptic Pool Modification (PSPM), that rely on stochastic weight updates in order to produce spikes within a short window of the desired times and eliminate spikes outside of this window. PSPM improves spike train similarity for all-to-all SNNs and makes no assumption about the post-synaptic potential of the neurons or the structure of the network since no gradients are required. We test whether optimizing for spike train similarity entails the discovery of accurate weights and explore the relative contributions of local and homeostatic weight updates. Although PSPM improves similarity between spike trains, the learned weights often differ from the weights of the ground truth model, implying that connectome inference from spike data may require additional constraints on connectivity statistics. We also find that spike train similarity is sensitive to local updates, but other measures of network activity such as avalanche distributions, can be learned through synaptic homeostasis.

Published
2020
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