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To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs).
Gradient-based learning algorithms for recurrent networks and their computational complexity
R. J. Williams and D. Zipser · 1995
Earlier work this paper cites.
Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Numerical linear algebra
Lloyd N Trefethen and David Bau III · 1997
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity
Răzvan V Florian · 2007
Earlier work this paper cites.
Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity
Michael A. Farries and Adrienne L. Fairhall · 2007
Earlier work this paper cites.
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
Robert Legenstein, Dejan Pecevski, and Wolfgang Maass · 2008
Earlier work this paper cites.
Timing is not everything: neuromodulation opens the stdp gate
Verena Pawlak, Jeffery R Wickens, Alfredo Kirkwood, and Jason ND Kerr · 2010
Earlier work this paper cites.
Stimulus selectivity in dorsal and ventral prefrontal cortex after training in working memory tasks
Travis Meyer, Xue-Lian Qi, Terrence R Stanford, and Christos Constantinidis · 2011
Earlier work this paper cites.
Conditional modulation of spike-timing-dependent plasticity for olfactory learning
Stijn Cassenaer and Gilles Laurent · 2012
Earlier work this paper cites.
Robustness and generalization
Huan Xu and Shie Mannor · 2012
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Context-dependent computation by recurrent dynamics in prefrontal cortex
Valerio Mante, David Sussillo, Krishna V Shenoy, and William T Newsome · 2013
Earlier work this paper cites.
The CaMKII/NMDAR complex as a molecular memory
Magdalena Sanhueza and John Lisman · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Inferring learning rules from distributions of firing rates in cortical neurons
Sukbin Lim, Jillian L McKee, Luke Woloszyn, Yali Amit, David J Freedman, David L Sheinberg, and Nicolas Brunel · 2015
Earlier work this paper cites.
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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Dopamine reward prediction-error signalling: a two-component response
Wolfram Schultz · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Astrocytes: Orchestrating synaptic plasticity?
Maurizio De Pittà, Nicolas Brunel, and Andrea Volterra · 2016
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Eigenvalues of the hessian in deep learning: Singularity and beyond
Levent Sagun, Leon Bottou, and Yann LeCun · 2016
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Gintare Karolina Dziugaite and Daniel M Roy · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Unbiased online recurrent optimization
Corentin Tallec and Yann Ollivier · 2017
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Empirical analysis of the hessian of over-parametrized neural networks
Levent Sagun, Utku Evci, V Ugur Guney, Yann Dauphin, and Leon Bottou · 2017
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Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2017
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Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
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Supervised learning in spiking neural networks with force training
Wilten Nicola and Claudia Clopath · 2017
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A bayesian perspective on generalization and stochastic gradient descent
Samuel L Smith and Quoc V Le · 2017
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full-force: A target-based method for training recurrent networks
Brian DePasquale, Christopher J Cueva, Kanaka Rajan, G Sean Escola, and LF Abbott · 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
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Control of synaptic plasticity in deep cortical networks
Pieter R. Roelfsema and Anthony Holtmaat · 2018
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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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Approximating real-time recurrent learning with random kronecker factors
Asier Mujika, Florian Meier, and Angelika Steger · 2018
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Kernel rnn learning (kernl)
Christopher Roth, Ingmar Kanitscheider, and Ila Fiete · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Sensitivity and generalization in neural networks: an empirical study
Roman Novak, Yasaman Bahri, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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Identifying generalization properties in neural networks
Huan Wang, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
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Hessian-based analysis of large batch training and robustness to adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney · 2018
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Finding flatter minima with sgd
Stanisław Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey · 2018
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Theory of deep learning iib: Optimization properties of sgd
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, and Tomaso Poggio · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, and Jinwen Ma · 2018
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Generalized leaky integrate-and-fire models classify multiple neuron types
Corinne Teeter, Ramakrishnan Iyer, Vilas Menon, Nathan Gouwens, David Feng, Jim Berg, Aaron Szafer, Nicholas Cain, Hongkui Zeng, Michael Hawrylycz, et al · 2018
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On the relation between the sharpest directions of dnn loss and the sgd step length
Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey · 2018
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Gradient descent for spiking neural networks
Dongsung Huh and Terrence J Sejnowski · 2018
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Superspike: Supervised learning in multilayer spiking neural networks
Friedemann Zenke and Surya Ganguli · 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
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Critical learning periods in deep networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2018
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Backpropagation through time and the brain
Timothy P Lillicrap and Adam Santoro · 2019
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2019
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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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Local online learning in recurrent networks with random feedback
James M Murray · 2019
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Using local plasticity rules to train recurrent neural networks
Owen Marschall, Kyunghyun Cho, and Cristina Savin · 2019
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Theories of Error Back-Propagation in the Brain
James C.R. Whittington and Rafal Bogacz · 2019
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Task representations in neural networks trained to perform many cognitive tasks
Guangyu Robert Yang, Madhura R Joglekar, H Francis Song, William T Newsome, and Xiao-Jing Wang · 2019
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Deep learning with asymmetric connections and hebbian updates
Yali Amit · 2019
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Beyond STDP — towards diverse and functionally relevant plasticity rules
Aparna Suvrathan · 2019
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Cortical credit assignment by hebbian, neuromodulatory and inhibitory plasticity
Johnatan Aljadeff, James D’amour, Rachel E Field, Robert C Froemke, and Claudia Clopath · 2019
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Neuromodulation of spike-timing-dependent plasticity: past, present, and future
Zuzanna Brzosko, Susanna B Mierau, and Ole Paulsen · 2019
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Single-cell transcriptomic evidence for dense intracortical neuropeptide networks
Stephen J Smith, Uygar Sümbül, Lucas T Graybuck, Forrest Collman, Sharmishtaa Seshamani, Rohan Gala, Olga Gliko, Leila Elabbady, Jeremy A Miller, Trygve E Bakken, et al · 2019
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On the variance of unbiased online recurrent optimization
Tim Cooijmans and James Martens · 2019
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Can sgd learn recurrent neural networks with provable generalization?
Zeyuan Allen-Zhu and Yuanzhi Li · 2019
Cited alongside, same era.
Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, and Riccardo Zecchina · 2019
Cited alongside, same era.
An investigation into neural net optimization via hessian eigenvalue density
Behrooz Ghorbani, Shankar Krishnan, and Ying Xiao · 2019
Cited alongside, same era.
The normalization method for alleviating pathological sharpness in wide neural networks
Ryo Karakida, Shotaro Akaho, and Shun-ichi Amari · 2019
Cited alongside, same era.
Fisher-rao metric, geometry, and complexity of neural networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, and James Stokes · 2019
Cited alongside, same era.
Tensor decompositions of higher-order correlations by nonlinear hebbian plasticity
Gabriel Ocker and Michael Buice · 2021
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Biological key-value memory networks
Danil Tyulmankov, Ching Fang, Annapurna Vadaparty, and Guangyu Robert Yang · 2021
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Credit assignment in neural networks through deep feedback control
Alexander Meulemans, Matilde Tristany Farinha, Javier Garcia Ordonez, Pau Vilimelis Aceituno, João Sacramento, and Benjamin F Grewe · 2021
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Learning rule influences recurrent network representations but not attractor structure in decision-making tasks
Brandon McMahan, Michael Kleinman, and Jonathan Kao · 2021
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Latent equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, and Mihai A Petrovici · 2021
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Akshay Rangamani, Nam H Nguyen, Abhishek Kumar, Dzung Phan, Sang H Chin, and Trac D Tran · 2019
Cited alongside, same era.
A mathematical theory of semantic development in deep neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2019
Cited alongside, same era.
Implicit regularization of discrete gradient dynamics in linear neural networks
Gauthier Gidel, Francis Bach, and Simon Lacoste-Julien · 2019
Cited alongside, same era.
How noise affects the hessian spectrum in overparameterized neural networks
Mingwei Wei and David J Schwab · 2019
Cited alongside, same era.
A critique of pure learning and what artificial neural networks can learn from animal brains
Anthony M Zador · 2019
Cited alongside, same era.
Towards explaining the regularization effect of initial large learning rate in training neural networks
Yuanzhi Li, Colin Wei, and Tengyu Ma · 2019
Cited alongside, same era.
Toward understanding the importance of noise in training neural networks
Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, and Tuo Zhao · 2019
Cited alongside, same era.
Towards biologically plausible convolutional networks
Roman Pogodin, Yash Mehta, Timothy Lillicrap, and Peter E Latham · 2021
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Neural optimal feedback control with local learning rules
Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan Sengupta, and Dmitri Chklovskii · 2021
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Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Bernd Illing, Jean Ventura, Guillaume Bellec, and Wulfram Gerstner · 2021
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Curriculum learning as a tool to uncover learning principles in the brain
Daniel R Kepple, Rainer Engelken, and Kanaka Rajan · 2021
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The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning
Grace Wan Yu Ang, Clara S Tang, Y Audrey Hay, Sara Zannone, Ole Paulsen, and Claudia Clopath · 2021
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A rapid and efficient learning rule for biological neural circuits
Eren Sezener, Agnieszka Grabska-Barwińska, Dimitar Kostadinov, Maxime Beau, Sanjukta Krishnagopal, David Budden, Marcus Hutter, Joel Veness, Matthew Botvinick, Claudia Clopath, et al · 2021
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Credit assignment through broadcasting a global error vector
David Clark, LF Abbott, and SueYeon Chung · 2021
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Inferring brain-wide interactions using data-constrained recurrent neural network models
Matthew G Perich, Charlotte Arlt, Sofia Soares, Megan E Young, Clayton P Mosher, Juri Minxha, Eugene Carter, Ueli Rutishauser, Peter H Rudebeck, Christopher D Harvey, et al · 2021
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Reverse engineering recurrent neural networks with jacobian switching linear dynamical systems
Jimmy Smith, Scott Linderman, and David Sussillo · 2021
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A mechanistic multi-area recurrent network model of decision-making
Michael Kleinman, Chandramouli Chandrasekaran, and Jonathan Kao · 2021
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Charting and navigating the space of solutions for recurrent neural networks
Elia Turner, Kabir V Dabholkar, and Omri Barak · 2021
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Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks
Robert Kim and Terrence J Sejnowski · 2021
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Adrian Valente, Srdjan Ostojic, and Jonathan Pillow · 2021
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Natural and artificial intelligence: A brief introduction to the interplay between ai and neuroscience research
Tom Macpherson, Anne Churchland, Terry Sejnowski, James DiCarlo, Yukiyasu Kamitani, Hidehiko Takahashi, and Takatoshi Hikida · 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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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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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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Convergence and alignment of gradient descent with random backpropagation weights
Ganlin Song, Ruitu Xu, and John Lafferty · 2021
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Convergence analysis and implicit regularization of feedback alignment for deep linear networks
Manuela Girotti, Ioannis Mitliagkas, and Gauthier Gidel · 2021
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Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C Courville, Doina Precup, and Guillaume Lajoie · 2021
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Implicit regularization via neural feature alignment
Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, and Simon Lacoste-Julien · 2021
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The inverse variance–flatness relation in stochastic gradient descent is critical for finding flat minima
Yu Feng and Yuhai Tu · 2021
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Hessian eigenspectra of more realistic nonlinear models
Zhenyu Liao and Michael W Mahoney · 2021
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Regularizing neural networks via adversarial model perturbation
Yaowei Zheng, Richong Zhang, and Yongyi Mao · 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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Gradient descent on neural networks typically occurs at the edge of stability
Jeremy M Cohen, Simran Kaur, Yuanzhi Li, J Zico Kolter, and Ameet Talwalkar · 2021
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A loss curvature perspective on training instabilities of deep learning models
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, and Orhan Firat · 2021
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Increasing liquid state machine performance with edge-of-chaos dynamics organized by astrocyte-modulated plasticity
Vladimir Ivanov and Konstantinos Michmizos · 2021
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Online learning of neural computations from sparse temporal feedback
Lukas Braun and Tim Vogels · 2021
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Neural population geometry reveals the role of stochasticity in robust perception
Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David Cox, Josh McDermott, James J DiCarlo, and SueYeon Chung · 2021
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Neural heterogeneity promotes robust learning
Nicolas Perez-Nieves, Vincent CH Leung, Pier Luigi Dragotti, and Dan FM Goodman · 2021
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Thalamic control of cortical dynamics in a model of flexible motor sequencing
Laureline Logiaco, LF Abbott, and Sean Escola · 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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Probabilistic skeletons endow brain-like neural networks with innate computing capabilities
Christoph Stöckl, Dominik Lang, and Wolfgang Maass · 2021
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Reverse engineering learned optimizers reveals known and novel mechanisms
Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, and Jascha Sohl-Dickstein · 2021
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Serotonin neurons modulate learning rate through uncertainty
Cooper D Grossman, Bilal A Bari, and Jeremiah Y Cohen · 2021
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Analysis of visual processing capabilities and neural coding strategies of a detailed model for laminar cortical microcircuits in mouse v1
Guozhang Chen, Franz Scherr, and Wolfgang Maass · 2021
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Training feedback spiking neural networks by implicit differentiation on the equilibrium state
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, and Zhouchen Lin · 2021
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Sparse spiking gradient descent
Nicolas Perez-Nieves and Dan Goodman · 2021
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Differentiable spike: Rethinking gradient-descent for training spiking neural networks
Yuhang Li, Yufei Guo, Shanghang Zhang, Shikuang Deng, Yongqing Hai, and Shi Gu · 2021
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Learning to time-decode in spiking neural networks through the information bottleneck
Nicolas Skatchkovsky, Osvaldo Simeone, and Hyeryung Jang · 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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Meta-learning synaptic plasticity and memory addressing for continual familiarity detection
Danil Tyulmankov, Guangyu Robert Yang, and LF Abbott · 2022
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Towards scaling difference target propagation by learning backprop targets
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Neurons learn by predicting future activity
Artur Luczak, Bruce L. McNaughton, and Yoshimasa Kubo · 2022
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Neuromodulators generate multiple context-relevant behaviors in a recurrent neural network by shifting activity hypertubes
Ben Tsuda, Stefan C Pate, Kay M Tye, Hava T Siegelmann, and Terrence J Sejnowski · 2022
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Probabilistic visual processing in humans and recurrent neural networks
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Latent circuit inference from heterogeneous neural responses during cognitive tasks
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Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
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On the power-law spectrum in deep learning: A bridge to protein science
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Questions for flat-minima optimization of modern neural networks
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Understanding gradient descent on edge of stability in deep learning
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Biological underpinnings for lifelong learning machines
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Goal-driven optimization of single-neuron properties in artificial networks reveals regularization role of neural diversity and adaptation
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Heterogeneity in neuronal dynamics is learned by gradient descent for temporal processing tasks
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Anticorrelated noise injection for improved generalization
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Investigating power laws in deep representation learning
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Surrogate gradients for analog neuromorphic computing
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