Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Chaudhuri, Rishidev"'
Autor:
Chaudhuri, Rishidev, Handebagh, Vivek
Chaos is generic in strongly-coupled recurrent networks of model neurons, and thought to be an easily accessible dynamical regime in the brain. While neural chaos is typically seen as an impediment to robust computation, we show how such chaos might
Externí odkaz:
http://arxiv.org/abs/2409.18329
Autor:
Yoo, S. J. Ben, El-Srouji, Luis, Datta, Suman, Yu, Shimeng, Incorvia, Jean Anne, Salleo, Alberto, Sorger, Volker, Hu, Juejun, Kimerling, Lionel C, Bouchard, Kristofer, Geng, Joy, Chaudhuri, Rishidev, Ranganath, Charan, O'Reilly, Randall
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the brain has been one of the top priorities of science and technology resea
Externí odkaz:
http://arxiv.org/abs/2403.19724
Bayesian interpretations of neural processing require that biological mechanisms represent and operate upon probability distributions in accordance with Bayes' theorem. Many have speculated that synaptic failure constitutes a mechanism of variational
Externí odkaz:
http://arxiv.org/abs/2210.01691
The Bayesian brain hypothesis postulates that the brain accurately operates on statistical distributions according to Bayes' theorem. The random failure of presynaptic vesicles to release neurotransmitters may allow the brain to sample from posterior
Externí odkaz:
http://arxiv.org/abs/2111.09780
Autor:
Moore, Eli, Chaudhuri, Rishidev
Publikováno v:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Many networks in the brain are sparsely connected, and the brain eliminates synapses during development and learning. How could the brain decide which synapses to prune? In a recurrent network, determining the importance of a synapse between two neur
Externí odkaz:
http://arxiv.org/abs/2011.07334
Autor:
Chaudhuri, Rishidev, Fiete, Ila
The brain must robustly store a large number of memories, corresponding to the many events encountered over a lifetime. However, the number of memory states in existing neural network models either grows weakly with network size or recall fails catas
Externí odkaz:
http://arxiv.org/abs/1704.02019
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Oct . 117(41), 25505-25516.
Externí odkaz:
https://www.jstor.org/stable/26969623
Akademický článek
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Autor:
Langdon, Angela J.1 (AUTHOR) alangdon@princeton.edu, Chaudhuri, Rishidev2 (AUTHOR) alangdon@princeton.edu
Publikováno v:
European Journal of Neuroscience. Jun2021, Vol. 53 Issue 11, p3511-3524. 14p.
Publikováno v:
Nature Neuroscience; 20240101, Issue: Preprints p1-9, 9p