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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 assignment problem.
Gradient-based learning algorithms for recurrent networks and their computational complexity
R. J. Williams and D. Zipser · 1995
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The mnist database of handwritten digits
Yann LeCun · 1998
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Competitive hebbian learning through spike-timing-dependent synaptic plasticity
Sen Song, Kenneth D Miller, and Larry F Abbott · 2000
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Theoretical neuroscience: computational and mathematical modeling of neural systems
Peter Dayan and Laurence F Abbott · 2001
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Spike timing-dependent plasticity: from synapse to perception
Yang Dan and Mu-Ming Poo · 2006
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Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity
Michael A. Farries and Adrienne L. Fairhall · 2007
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Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity
Răzvan V Florian · 2007
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Timing is not everything: neuromodulation opens the stdp gate
Verena Pawlak, Jeffery R Wickens, Alfredo Kirkwood, and Jason ND Kerr · 2010
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Conditional modulation of spike-timing-dependent plasticity for olfactory learning
Stijn Cassenaer and Gilles Laurent · 2012
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Neuropeptide Transmission in Brain Circuits
Anthony N. van den Pol · 2012
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Dopaminergic Modulation of Synaptic Transmission in Cortex and Striatum
Nicolas X. Tritsch and Bernardo L. Sabatini · 2012
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Neuromodulation of neuronal circuits: back to the future
Eve Marder · 2012
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The CaMKII/NMDAR complex as a molecular memory
Magdalena Sanhueza and John Lisman · 2013
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Neuropeptides in learning and memory
Éva Borbély, Bálint Scheich, and Zsuzsanna Helyes · 2013
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Modulation of distal calcium electrogenesis by neuropeptide Y1 receptors inhibits neocortical long-term depression
Trevor J. Hamilton, Sara Xapelli, Sheldon D. Michaelson, Matthew E. Larkum, and William F. Colmers · 2013
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A critical time window for dopamine actions on the structural plasticity of dendritic spines
Sho Yagishita, Akiko Hayashi-Takagi, Graham C.R. Ellis-Davies, Hidetoshi Urakubo, Shin Ishii, and Haruo Kasai · 2014
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Neuronal reward and decision signals: from theories to data
Wolfram Schultz · 2015
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
Nicolas Frémaux and Wulfram Gerstner · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
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Dopamine reward prediction-error signalling: a two-component response
Wolfram Schultz · 2016
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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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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
Nicolas Frémaux and Wulfram Gerstner · 2016
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Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules
Wulfram Gerstner, Marco Lehmann, Vasiliki Liakoni, Dane Corneil, and Johanni Brea · 2018
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Shared and distinct transcriptomic cell types across neocortical areas
Bosiljka Tasic, Zizhen Yao, Lucas T. Graybuck, Kimberly A. Smith, Thuc Nghi Nguyen, Darren Bertagnolli, Jeff Goldy, Emma Garren, Michael N. Economo, Sarada Viswanathan, Osnat Penn, Trygve Bakken, Vilas Menon, Jeremy Miller, Olivia Fong, Karla E. Hirokawa, Kanan Lathia, Christine Rimorin, Michael Tieu, Rachael Larsen, Tamara Casper, Eliza Barkan, Matthew Kroll, Sheana Parry, Nadiya V. Shapovalova, Daniel Hirschstein, Julie Pendergraft, Heather A. Sullivan, Tae Kyung Kim, Aaron Szafer, Nick Dee, Peter Groblewski, Ian Wickersham, Ali Cetin, Julie A. Harris, Boaz P. Levi, Susan M. Sunkin, Linda Madisen, Tanya L. Daigle, Loren Looger, Amy Bernard, John Phillips, Ed Lein, Michael Hawrylycz, Karel Svoboda, Allan R. Jones, Christof Koch, and Hongkui Zeng · 2018
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Control of synaptic plasticity in deep cortical networks
Pieter R. Roelfsema and Anthony Holtmaat · 2018
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A biologically plausible learning rule for deep learning in the brain
Isabella Pozzi, Sander Bohté, and Pieter Roelfsema · 2018
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Dendritic cortical microcircuits approximate the backpropagation algorithm
João Sacramento, Rui Ponte Costa, Yoshua Bengio, and Walter Senn · 2018
Backpropagation through time and the brain
Timothy P Lillicrap and Adam Santoro · 2019
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Backpropagation and the brain
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, and Geoffrey Hinton · 2020
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A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, and Wolfgang Maass · 2020
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Synaptic Plasticity Forms and Functions
Jeffrey C. Magee and Christine Grienberger · 2020
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A unified framework of online learning algorithms for training recurrent neural networks
Owen Marschall, Kyunghyun Cho, and Cristina Savin · 2020
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The role of serotonin in learning and memory: a rich pallet of experimental studies
Jean-Christophe Cassel · 2020
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Approximating Real-Time Recurrent Learning with Random Kronecker Factors
Asier Mujika, Florian Meier, and Angelika Steger · 2018
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Unbiased Online Recurrent Optimization
Corentin Tallec and Yann Ollivier · 2018
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Approximating real-time recurrent learning with random kronecker factors
Asier Mujika, Florian Meier, and Angelika Steger · 2018
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Feedback through graph motifs relates structure and function in complex networks
Yu Hu, Steven L Brunton, Nicholas Cain, Stefan Mihalas, J Nathan Kutz, and Eric Shea-Brown · 2018
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Long short-term memory and learning-to-learn in networks of spiking neurons
Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, and Wolfgang Maass · 2018
Cited alongside, same era.
A deep learning framework for neuroscience
Blake A Richards, Timothy P Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy de Berker, Surya Ganguli, et al · 2019
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Backpropagation through time and the brain
Timothy P. Lillicrap and Adam Santoro · 2019
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Local online learning in recurrent networks with random feedback
James M. Murray · 2019
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New light on cortical neuropeptides and synaptic network plasticity
Stephen J. Smith, Michael Hawrylycz, Jean Rossier, and Uygar Sümbül · 2020
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Kernelized information bottleneck leads to biologically plausible 3-factor hebbian learning in deep networks
Roman Pogodin and Peter Latham · 2020
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Stable and expressive recurrent vision models
Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan, Rex Liu, and Thomas Serre · 2020
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Activation relaxation: A local dynamical approximation to backpropagation in the brain
Beren Millidge, Alexander Tschantz, Anil K Seth, and Christopher L Buckley · 2020
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A practical sparse approximation for real time recurrent learning
Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, and Alex Graves · 2020
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Brain-inspired learning on neuromorphic substrates
Friedemann Zenke and Emre O Neftci · 2020
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Bombesin-like peptide recruits disinhibitory cortical circuits and enhances fear memories
Sarah Melzer, Elena Newmark, Grace Or Mizuno, Minsuk Hyun, Adrienne C Philson, Eleonora Quiroli, Beatrice Righetti, Malika R Gregory, Kee Wui Huang, James Levasseur, et al · 2020
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Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network
Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, and Uygar Sümbül · 2021
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The credit assignment problem in cortico-basal ganglia-thalamic networks: A review, a problem and a possible solution
Jonathan E Rubin, Catalina Vich, Matthew Clapp, Kendra Noneman, and Timothy Verstynen · 2021
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Transcriptomic evidence for dense peptidergic networks within forebrains of four widely divergent tetrapods
Stephen J Smith · 2021
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, Blake A Richards, and Richard Naud · 2021
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Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, and Damien Querlioz · 2021
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Psychrnn: An accessible and flexible python package for training recurrent neural network models on cognitive tasks
Daniel B Ehrlich, Jasmine T Stone, David Brandfonbrener, Alexander Atanasov, and John D Murray · 2021
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Next-generation of recurrent neural network models for cognition
Guangyu Robert Yang and Manuel Molano Mazon · 2021
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A solution to temporal credit assignment using cell-type-specific modulatory signals
Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, and Uygar Sümbül · 2021
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Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Yuhan Helena Liu, Arna Ghosh, Blake A Richards, Eric Shea-Brown, and Guillaume Lajoie · 2022
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