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pro vyhledávání: '"Liu, Yuhan Helena"'
The impact of initial connectivity on learning has been extensively studied in the context of backpropagation-based gradient descent, but it remains largely underexplored in biologically plausible learning settings. Focusing on recurrent neural netwo
Externí odkaz:
http://arxiv.org/abs/2410.11164
Training networks consisting of biophysically accurate neuron models could allow for new insights into how brain circuits can organize and solve tasks. We begin by analyzing the extent to which the central algorithm for neural network learning -- sto
Externí odkaz:
http://arxiv.org/abs/2311.10869
Autor:
Liu, Yuhan Helena, Baratin, Aristide, Cornford, Jonathan, Mihalas, Stefan, Shea-Brown, Eric, Lajoie, Guillaume
In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp.
Externí odkaz:
http://arxiv.org/abs/2310.08513
The spectacular successes of recurrent neural network models where key parameters are adjusted via backpropagation-based gradient descent have inspired much thought as to how biological neuronal networks might solve the corresponding synaptic credit
Externí odkaz:
http://arxiv.org/abs/2206.01338
To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond task accuracy, it is unclear if such learning rules converge to solutio
Externí odkaz:
http://arxiv.org/abs/2206.00823
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Dec . 118(51), 1-11.
Externí odkaz:
https://www.jstor.org/stable/27117564
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Publikováno v:
BioRxiv : the preprint server for biology [bioRxiv] 2024 May 15. Date of Electronic Publication: 2024 May 15.
Publikováno v:
ArXiv [ArXiv] 2024 Feb 19. Date of Electronic Publication: 2024 Feb 19.
Publikováno v:
ArXiv [ArXiv] 2023 Nov 21. Date of Electronic Publication: 2023 Nov 21.