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Credit Assignment Through Broadcasting a Global Error Vector


Jun 08, 2021
David G. Clark, L. F. Abbott, SueYeon Chung

* 18 pages, 6 figures 

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Neural population geometry: An approach for understanding biological and artificial neural networks


Apr 17, 2021
SueYeon Chung, L. F. Abbott


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Training dynamically balanced excitatory-inhibitory networks


Dec 29, 2018
Alessandro Ingrosso, L. F. Abbott

* 12 pages, 7 figures 

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Feedback alignment in deep convolutional networks


Dec 12, 2018
Theodore H. Moskovitz, Ashok Litwin-Kumar, L. F. Abbott


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full-FORCE: A Target-Based Method for Training Recurrent Networks


Oct 09, 2017
Brian DePasquale, Christopher J. Cueva, Kanaka Rajan, G. Sean Escola, L. F. Abbott

* 20 pages, 8 figures 

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Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity


May 03, 2017
Ran Rubin, L. F. Abbott, Haim Sompolinsky

* Proceedings of the National Academy of Sciences of the United States of America, 114(41), 2017 
* Article and supplementary information 

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LFADS - Latent Factor Analysis via Dynamical Systems


Aug 22, 2016
David Sussillo, Rafal Jozefowicz, L. F. Abbott, Chethan Pandarinath

* 16 pages, 11 figures 

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Random Walk Initialization for Training Very Deep Feedforward Networks


Feb 27, 2015
David Sussillo, L. F. Abbott

* 10 pages, 4 figures 

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