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In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients.
Competitive learning: From interactive activation to adaptive resonance
Stephen Grossberg · 1987
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The recent excitement about neural networks
Francis Crick · 1989
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Backpropagation without weight transport
John F Kolen and Jordan B Pollack · 1994
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Attention-gated reinforcement learning of internal representations for classification
Pieter R Roelfsema and Arjen van Ooyen · 2005
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Mostly harmless econometrics: An empiricist’s companion
Joshua D Angrist and Jörn-Steffen Pischke · 2008
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Error correction, sensory prediction, and adaptation in motor control
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Random synaptic feedback weights support error backpropagation for deep learning
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2016
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Feedback alignment in deep convolutional networks
Theodore H Moskovitz, Ashok Litwin-Kumar, and LF Abbott · 2018
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Dendritic cortical microcircuits approximate the backpropagation algorithm
João Sacramento, Rui Ponte Costa, Yoshua Bengio, and Walter Senn · 2018
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Using weight mirrors to improve feedback alignment
Mohamed Akrout, Collin Wilson, Peter C Humphreys, Timothy Lillicrap, and Douglas Tweed · 2019
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Loss landscapes of regularized linear autoencoders
Daniel Kunin, Jonathan M Bloom, Aleksandrina Goeva, and Cotton Seed · 2019
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Spiking allows neurons to estimate their causal effect
Benjamin James Lansdell and Konrad Paul Kording · 2019
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Ioana E Marinescu, Patrick N Lawlor, and Konrad P Kording · 2018
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Dale’s principle
Piergiorgio Strata and Robin Harvey
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Fundamental bounds on learning performance in neural circuits
Dhruva Venkita Raman, Adriana Perez Rotondo, and Timothy O’Leary · 2019
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