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pro vyhledávání: '"Perich, Matthew G"'
Autor:
Williams, Ezekiel, Ryoo, Avery Hee-Woon, Jiralerspong, Thomas, Payeur, Alexandre, Perich, Matthew G., Mazzucato, Luca, Lajoie, Guillaume
Landmark universal function approximation results for neural networks with trained weights and biases provided impetus for the ubiquitous use of neural networks as learning models in Artificial Intelligence (AI) and neuroscience. Recent work has push
Externí odkaz:
http://arxiv.org/abs/2407.00957
Autor:
Azabou, Mehdi, Arora, Vinam, Ganesh, Venkataramana, Mao, Ximeng, Nachimuthu, Santosh, Mendelson, Michael J., Richards, Blake, Perich, Matthew G., Lajoie, Guillaume, Dyer, Eva L.
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as ea
Externí odkaz:
http://arxiv.org/abs/2310.16046
Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machine interfaces. Reliable decoding even from small neural populations is
Externí odkaz:
http://arxiv.org/abs/2205.09829
Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays. Therefore, predicting behaviour from neural population recordings has
Externí odkaz:
http://arxiv.org/abs/2202.06159
Autor:
Hurwitz, Cole, Srivastava, Akash, Xu, Kai, Jude, Justin, Perich, Matthew G., Miller, Lee E., Hennig, Matthias H.
Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously measured behaviour into these models to further disentangle sources of ne
Externí odkaz:
http://arxiv.org/abs/2110.14853
Autor:
Glaser, Joshua I., Benjamin, Ari S., Chowdhury, Raeed H., Perich, Matthew G., Miller, Lee E., Kording, Konrad P.
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding perfo
Externí odkaz:
http://arxiv.org/abs/1708.00909
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Publikováno v:
Kudryashova, N, Perich, M G, Miller, L E & Hennig, M H 2023, ' Ctrl-TNDM: Decoding feedback-driven movement corrections from motor cortex neurons ', Computational and Systems Neuroscience (Cosyne) 2023, Montréal, Canada, 9/03/23-12/03/23 .
Recent studies of motor control have shown that the neural population activity in motor cortical areas has a low-dimensional structure: a low number of latent dynamical factors that explain a large fraction of neural variability. It is unclear, howev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3094::5faeb0a3818765aeaae58ebca9d01e4b
https://hdl.handle.net/20.500.11820/5f6f3d17-22a5-46b9-b9a5-03065c83487d
https://hdl.handle.net/20.500.11820/5f6f3d17-22a5-46b9-b9a5-03065c83487d