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In humans, perceptual awareness facilitates the fast recognition and extraction of information from sensory input.
A. Wald, “An essentially complete class of admissible decision functions,” The Annals of Mathematical Statistics , pp. 549–555, 1947
1947
Earlier work this paper cites.
D. E. Berlyne, “Curiosity and exploration,” Science , vol. 153, no. 3731, pp. 25–33, 1966
1966
Earlier work this paper cites.
L. D. Brown, “A complete class theorem for statistical problems with finite sample spaces,” The Annals of Statistics , pp. 1289–1300, 1981
1981
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature , vol. 323, no. 6088, pp. 533–536, 1986
1986
Earlier work this paper cites.
R. S. Sutton, “Integrated architectures for learning, planning, and reacting based on approximating dynamic programming,” in Machine learning proceedings 1990 . Elsevier, 1990, pp. 216–224
1990
Earlier work this paper cites.
J. R. Movellan, “Contrastive hebbian learning in the continuous hopfield model,” in Connectionist Models . Elsevier, 1991, pp. 10–17
1991
Earlier work this paper cites.
P. Mazzoni, R. A. Andersen, and M. I. Jordan, “A more biologically plausible learning rule for neural networks.” Proceedings of the National Academy of Sciences , vol. 88, no. 10, pp. 4433–4437, 1991
1991
Earlier work this paper cites.
J. Schmidhuber, “A possibility for implementing curiosity and boredom in model-building neural controllers,” in Proc. of the international conference on simulation of adaptive behavior: From animals to animats , 1991, pp. 222–227
1991
Earlier work this paper cites.
A. W. Moore, “Variable resolution dynamic programming: Efficiently learning action maps in multivariate real-valued state-spaces,” in Machine Learning Proceedings 1991 . Elsevier, 1991, pp. 333–337
1991
Earlier work this paper cites.
C. D. Spielberger and L. M. Starr, “Curiosity and exploratory behavior,” Motivation: Theory and research , pp. 221–243, 1994
1994
Earlier work this paper cites.
P. R. Montague and T. J. Sejnowski, “The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms.” Learning & Memory , vol. 1, no. 1, pp. 1–33, 1994
1994
Earlier work this paper cites.
J. Storck, S. Hochreiter, and J. Schmidhuber, “Reinforcement driven information acquisition in non-deterministic environments,” in Proceedings of the international conference on artificial neural networks, Paris , vol. 2. Citeseer, 1995, pp. 159–164
1995
Earlier work this paper cites.
R. C. O’Reilly, “Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm,” Neural computation , vol. 8, no. 5, pp. 895–938, 1996
1996
Earlier work this paper cites.
P. R. Montague, P. Dayan, and T. J. Sejnowski, “A framework for mesencephalic dopamine systems based on predictive hebbian learning,” Journal of neuroscience , vol. 16, no. 5, pp. 1936–1947, 1996
1996
Earlier work this paper cites.
R. Y. Rubinstein, “Optimization of computer simulation models with rare events,” European Journal of Operational Research , vol. 99, no. 1, pp. 89–112, 1997
1997
Earlier work this paper cites.
W. Schultz, P. Dayan, and P. R. Montague, “A neural substrate of prediction and reward,” Science , vol. 275, no. 5306, pp. 1593–1599, 1997
1997
Earlier work this paper cites.
R. C. O’Reilly, “Six principles for biologically based computational models of cortical cognition,” Trends in cognitive sciences , vol. 2, no. 11, pp. 455–462, 1998
1998
Earlier work this paper cites.
R. P. Rao and D. H. Ballard, “Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.” Nature neuroscience , vol. 2, no. 1, 1999
1999
Earlier work this paper cites.
R. M. Ryan and E. L. Deci, “Intrinsic and extrinsic motivations: Classic definitions and new directions,” Contemporary educational psychology , vol. 25, no. 1, pp. 54–67, 2000
2000
Earlier work this paper cites.
S. P. Singh, A. G. Barto, and N. Chentanez, “Intrinsically motivated reinforcement learning,” in NIPS , 2004
2004
Earlier work this paper cites.
W. Zhou and R. Coggins, “Biologically inspired reinforcement learning: Reward-based decomposition for multi-goal environments,” in International Workshop on Biologically Inspired Approaches to Advanced Information Technology . Springer, 2004, pp. 80–94
2004
Earlier work this paper cites.
K. Friston, “A theory of cortical responses,” Philosophical transactions of the Royal Society B: Biological sciences , vol. 360, no. 1456, pp. 815–836, 2005
2005
Earlier work this paper cites.
G. G. Turrigiano, “The self-tuning neuron: synaptic scaling of excitatory synapses,” Cell , vol. 135, no. 3, pp. 422–435, 2008
2008
Earlier work this paper cites.
K. Ibata, Q. Sun, and G. G. Turrigiano, “Rapid synaptic scaling induced by changes in postsynaptic firing,” Neuron , vol. 57, no. 6, pp. 819–826, 2008
2008
Earlier work this paper cites.
Y. Niv, “Reinforcement learning in the brain,” Journal of Mathematical Psychology , vol. 53, no. 3, pp. 139–154, 2009
2009
Earlier work this paper cites.
V. Heidrich-Meisner and C. Igel, “Neuroevolution strategies for episodic reinforcement learning,” Journal of Algorithms , vol. 64, no. 4, pp. 152–168, 2009, special Issue: Reinforcement Learning
2009
Earlier work this paper cites.
K. Friston, “The free-energy principle: a rough guide to the brain?” Trends in cognitive sciences , vol. 13, no. 7, pp. 293–301, 2009
2009
Earlier work this paper cites.
K. J. Friston, J. Daunizeau, and S. J. Kiebel, “Reinforcement learning or active inference?” PloS one , vol. 4, no. 7, p. e6421, 2009
2009
Earlier work this paper cites.
P.-Y. Oudeyer and F. Kaplan, “What is intrinsic motivation? a typology of computational approaches,” Frontiers in neurorobotics , vol. 1, p. 6, 2009
2009
Earlier work this paper cites.
K. Friston and S. Kiebel, “Predictive coding under the free-energy principle,” Philosophical Transactions of the Royal Society B: Biological Sciences , vol. 364, no. 1521, pp. 1211–1221, 2009
2009
Earlier work this paper cites.
J. O’Neill, B. Pleydell-Bouverie, D. Dupret, and J. Csicsvari, “Play it again: reactivation of waking experience and memory,” Trends in neurosciences , vol. 33, no. 5, pp. 220–229, 2010
2010
Earlier work this paper cites.
K. Friston, “The free-energy principle: a unified brain theory?” Nature reviews neuroscience , vol. 11, no. 2, pp. 127–138, 2010
2010
Earlier work this paper cites.
J. Daunizeau, H. E. Den Ouden, M. Pessiglione, S. J. Kiebel, K. E. Stephan, and K. J. Friston, “Observing the observer (i): meta-bayesian models of learning and decision-making,” PloS one , vol. 5, no. 12, p. e15554, 2010
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
K. Friston, J. Mattout, and J. Kilner, “Action understanding and active inference,” Biological cybernetics , vol. 104, no. 1, pp. 137–160, 2011
2011
Earlier work this paper cites.
A. Solway and M. M. Botvinick, “Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates.” Psychological review , vol. 119, no. 1, p. 120, 2012
2012
Cited alongside, same era.
M. Botvinick and M. Toussaint, “Planning as inference,” Trends in cognitive sciences , vol. 16, no. 10, pp. 485–488, 2012
2012
Cited alongside, same era.
T. Tieleman and G. Hinton, “Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude,” COURSERA: Neural Networks for Machine Learning, 2012
2012
Cited alongside, same era.
K. Friston, S. Samothrakis, and R. Montague, “Active inference and agency: optimal control without cost functions,” Biological cybernetics , vol. 106, no. 8, pp. 523–541, 2012
2012
Cited alongside, same era.
A. M. Bastos, W. M. Usrey, R. A. Adams, G. R. Mangun, P. Fries, and K. J. Friston, “Canonical microcircuits for predictive coding,” Neuron , vol. 76, no. 4, pp. 695–711, 2012
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel et al. , “A general reinforcement learning algorithm that masters chess, shogi, and go through self-play,” Science , vol. 362, no. 6419, pp. 1140–1144, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
K. J. Friston, R. Rosch, T. Parr, C. Price, and H. Bowman, “Deep temporal models and active inference,” Neuroscience & Biobehavioral Reviews , vol. 90, pp. 486–501, 2018
2018
Later among the works it cites.
K. Ueltzhöffer, “Deep active inference,” Biological cybernetics , vol. 112, no. 6, pp. 547–573, 2018
2018
Later among the works it cites.
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2012
Cited alongside, same era.
C. Wacongne, J.-P. Changeux, and S. Dehaene, “A neuronal model of predictive coding accounting for the mismatch negativity,” Journal of Neuroscience , vol. 32, no. 11, pp. 3665–3678, 2012
2012
Cited alongside, same era.
P. J. Silvia, “Curiosity and motivation,” The Oxford handbook of human motivation , pp. 157–166, 2012
2012
Cited alongside, same era.
2013
Cited alongside, same era.
A. Barto, M. Mirolli, and G. Baldassarre, “Novelty or surprise?” Frontiers in psychology , vol. 4, p. 907, 2013
2013
Cited alongside, same era.
K. C. Rawlik, “On probabilistic inference approaches to stochastic optimal control,” Ph.D. dissertation, The University of Edinburgh, 2013, edinburgh, Scotland
2013
Cited alongside, same era.
2014
Cited alongside, same era.
K. Friston, F. Rigoli, D. Ognibene, C. Mathys, T. Fitzgerald, and G. Pezzulo, “Active inference and epistemic value,” Cognitive neuroscience , vol. 6, no. 4, pp. 187–214, 2015
2015
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Marino, Y. Yue, and S. Mandt, “Iterative amortized inference,” in International Conference on Machine Learning . PMLR, 2018, pp. 3403–3412
2018
Later among the works it cites.
W. H. Alexander and J. W. Brown, “Frontal cortex function as derived from hierarchical predictive coding,” Scientific reports , vol. 8, no. 1, pp. 1–11, 2018
2018
Later among the works it cites.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International Conference on Machine Learning . PMLR, 2018, pp. 1861–1870
2018
Later among the works it cites.
B. O’Donoghue, I. Osband, R. Munos, and V. Mnih, “The uncertainty bellman equation and exploration,” in International Conference on Machine Learning , 2018, pp. 3836–3845
2018
Later among the works it cites.
A. Nagabandi, G. Kahn, R. S. Fearing, and S. Levine, “Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 7559–7566
2018
Later among the works it cites.
2018
Later among the works it cites.
T. Wei and B. Webb, “A bio-inspired reinforcement learning rule to optimise dynamical neural networks for robot control,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 556–561
2018
Later among the works it cites.
A. M. Zador, “A critique of pure learning and what artificial neural networks can learn from animal brains,” Nature communications , vol. 10, no. 1, pp. 1–7, 2019
2019
Later among the works it cites.
A. G. Ororbia and A. Mali, “Biologically motivated algorithms for propagating local target representations,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 4651–4658
2019
Later among the works it cites.
2019
Later among the works it cites.
P. Shyam, W. Jaśkowski, and F. Gomez, “Model-based active exploration,” in International Conference on Machine Learning . PMLR, 2019, pp. 5779–5788
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in International Conference on Machine Learning . PMLR, 2019, pp. 2555–2565
2019
Later among the works it cites.
A. Tschantz, A. K. Seth, and C. L. Buckley, “Learning action-oriented models through active inference,” PLoS computational biology , vol. 16, no. 4, p. e1007805, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Tschantz, M. Baltieri, A. K. Seth, and C. L. Buckley, “Scaling active inference,” in 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2020, pp. 1–8
2020
Later among the works it cites.
A. Ororbia, A. Mali, C. L. Giles, and D. Kifer, “Continual learning of recurrent neural networks by locally aligning distributed representations,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Later among the works it cites.
N. Manchev and M. W. Spratling, “Target propagation in recurrent neural networks.” Journal of Machine Learning Research , vol. 21, no. 7, pp. 1–33, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Ororbia, A. Mali, D. Kifer, and C. L. Giles, “Large-scale gradient-free deep learning with recursive local representation alignment,” arXiv e-prints , pp. arXiv–2002, 2020
2020
Later among the works it cites.
R. Sekar, O. Rybkin, K. Daniilidis, P. Abbeel, D. Hafner, and D. Pathak, “Planning to explore via self-supervised world models,” in International Conference on Machine Learning . PMLR, 2020, pp. 8583–8592
2020
Later among the works it cites.
B. Millidge, “Deep active inference as variational policy gradients,” Journal of Mathematical Psychology , vol. 96, p. 102348, 2020
2020
Later among the works it cites.
H. Yamakawa, “Attentional reinforcement learning in the brain,” New Generation Computing , vol. 38, no. 1, pp. 49–64, 2020
2020
Later among the works it cites.
T. C. Moulin, D. Rayêe, M. J. Williams, and H. B. Schiöth, “The synaptic scaling literature: A systematic review of methodologies and quality of reporting,” Frontiers in cellular neuroscience , vol. 14, p. 164, 2020
2020
Later among the works it cites.