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Lifelong learning and adaptability are two defining aspects of biological agents.
Weight Agnostic Neural Networks
Adam Gaier and David Ha · 1906
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The organization of behavior; a neuropsychological theory
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The baldwin effect
George Gaylord Simpson · 1953
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Brain and intelligence in vertebrates
Euan M Macphail · 1982
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Learning a synaptic learning rule
Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier · 1991
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Learning to control fast-weight memories: an alternative to dynamic recurrent networks
Jürgen Schmidhuber · 1992
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Evolving dynamical neural networks for adaptive behavior
Randall D Beer and John C Gallagher · 1992
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Learning to learn: Introduction and overview
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Synaptic plasticity and memory: an evaluation of the hypothesis
Stephen J Martin, Paul D Grimwood, and Richard GM Morris · 2000
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Evolutionary robots with on-line self-organization and behavioral fitness
Dario Floreano and Joseba Urzelai · 2000
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Rate, timing, and cooperativity jointly determine cortical synaptic plasticity
P. J. Sjöström, G. G. Turrigiano, and S. B. Nelson · 2001
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Evolution of reinforcement learning in uncertain environments: Emergence of risk-aversion and matching
Yael Niv, Daphna Joel, Isaac Meilijson, and Eytan Ruppin · 2001
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Real-time computing without stable states: A new framework for neural computation based on perturbations
Wolfgang Maass, Thomas Natschläger, and Henry Markram · 2002
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Evolution strategies–a comprehensive introduction
Hans-Georg Beyer and Hans-Paul Schwefel · 2002
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Self-organization in biological systems , volume 7
Scott Camazine, Jean-Louis Deneubourg, Nigel R Franks, James Sneyd, Eric Bonabeau, and Guy Theraula · 2003
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Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
Herbert Jaeger and Harald Haas · 2004
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By carrot or by stick: cognitive reinforcement learning in parkinsonism
Michael J Frank, Lauren C Seeberger, and Randall C O’reilly · 2004
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Training recurrent networks by evolino
Jürgen Schmidhuber, Daan Wierstra, Matteo Gagliolo, and Faustino Gomez · 2007
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Evolving neuromodulatory topologies for reinforcement learning-like problems
Andrea Soltoggio, Peter Durr, Claudio Mattiussi, and Dario Floreano · 2007
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Spike-Rate Coding and Spike-Time Coding Are Affected Oppositely by Different Adaptation Mechanisms
Steven A. Prescott and Terrence J. Sejnowski · 2008
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Synaptic Learning Rules and Sparse Coding in a Model Sensory System
Luca A. Finelli, Seth Haney, Maxim Bazhenov, Mark Stopfer, and Terrence J. Sejnowski · 2008
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Juergen Schmidhuber · 2008
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Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios
Andrea Soltoggio, John A Bullinaria, Claudio Mattiussi, Peter Dürr, and Dario Floreano · 2008
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Indirectly Encoding Neural Plasticity as a Pattern of Local Rules
Sebastian Risi and Kenneth O. Stanley · 2010
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Optogenetic stimulation of a hippocampal engram activates fear memory recall
Xu Liu, Steve Ramirez, Petti T Pang, Corey B Puryear, Arvind Govindarajan, Karl Deisseroth, and Susumu Tonegawa · 2012
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Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control , volume 70
Stephen T Grossberg · 2012
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Mujoco: A physics engine for model-based control
Dendritic cortical microcircuits approximate the backpropagation algorithm
João Sacramento, Rui Ponte Costa, Yoshua Bengio, and Walter Senn · 2018
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Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks
Andrea Soltoggio, Kenneth O Stanley, and Sebastian Risi · 2018
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Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi, Jeff Clune, and Kenneth O. Stanley · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 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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Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Random feedback weights support learning in deep neural networks
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2014
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Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain
Romain Brette · 2015
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High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Using fast weights to attend to the recent past
Jimmy Ba, Geoffrey E Hinton, Volodymyr Mnih, Joel Z Leibo, and Catalin Ionescu · 2016
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Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, and Andrei A Rusu · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle and Michael Carbin · 2018
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 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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Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules
Jeffrey Cheng, Ari Benjamin, Benjamin Lansdell, and Konrad Paul Kording · 2019
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A critique of pure learning and what artificial neural networks can learn from animal brains
Anthony M Zador · 2019
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Deep neuroevolution of recurrent and discrete world models
Sebastian Risi and Kenneth O Stanley · 2019
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Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Evolving inborn knowledge for fast adaptation in dynamic pomdp problems
Eseoghene Ben-Iwhiwhu, Pawel Ladosz, Jeffery Dick, Wen-Hua Chen, Praveen Pilly, and Andrea Soltoggio · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Growing neural cellular automata
Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson, and Michael Levin · 2020
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Neuroevolution of self-interpretable agents
Yujin Tang, Duong Nguyen, and David Ha · 2020
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Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
Thomas Miconi, Aditya Rawal, Jeff Clune, and Kenneth O Stanley · 2020
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