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Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence.
Neural networks and physical systems with emergent collective computational abilities
Hopfield, J. J. (1982) · 1982
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Reconstructive memory: A computer model
Kolodner, J. L. (1983) · 1983
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Information capacity of the hopfield model
Abu-Mostafa, Y. and Jacques, J. S. (1985) · 1985
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The hebb rule for synaptic plasticity: algorithms and implementations
Sejnowski, T. J. and Tesauro, G. (1989) · 1989
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Associative memories via artificial neural networks
Michel, A. N. and Farrell, J. A. (1990) · 1990
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Stochastic differential equations
Kloeden, P. E., Platen, E., Kloeden, P. E., and Platen, E. (1992) · 1992
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A bayesian interpretation for the hopfield network
Hancock, E. R. and Kittler, J. (1993) · 1993
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Creating false memories: Remembering words not presented in lists
Roediger, H. L. and McDermott, K. B. (1995) · 1995
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Suppression of synaptic transmission may allow combination of associative feedback and self-organizing feedforward connections in the neocortex
Hasselmo, M. E. and Cekic, M. (1996) · 1996
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Noradrenergic suppression of synaptic transmission may influence cortical signal-to-noise ratio
Hasselmo, M. E., Linster, C., Patil, M., Ma, D., and Cekic, M. (1997) · 1997
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rao, R. P. N. and Ballard, D. H. (1999) · 1999
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Septal modulation of hippocampal dynamics: what is the function of the theta rhythm?
Hasselmo, M. E. (2000) · 2000
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A free energy principle for the brain
Friston, K., Kilner, J., and Harrison, L. (2006) · 2006
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Remembering the past to imagine the future: the prospective brain
Schacter, D. L., Addis, D. R., and B., R. L. (2007) · 2007
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A bayesian account of reconstructive memory
Hemmer, P. and Steyvers, M. (2009) · 2009
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The free-energy principle: a unified brain theory?
Friston, K. (2010) · 2010
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Reactivation, replay, and preplay: how it might all fit together
Buhry, L., Azizi, A. H., Cheng, S., et al. (2011) · 2011
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An introduction to restricted boltzmann machines
Fischer, A. and Igel, C. (2012) · 2012
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Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
Güçlü, U. and van Gerven, M. A. J. (2015) · 2015
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On the persuadability of memory: Is changing people’s memories no more than changing their minds?
Nash, R. A., W., R. L., and Hope, L. (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Improved schemes for episodic memory-based lifelong learning
Guo, Y., Liu, M., Yang, T., and Rosing, T. (2020) · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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From prediction to imagination
Jones, M. and Wilkinson, S. (2020) · 2020
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Review and analysis of zero, one and few shot learning approaches
Kadam, S. and Vaidya, V. (2020) · 2020
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Large associative memory problem in neurobiology and machine learning
Krotov, D. and Hopfield, J. (2021) · 2021
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Predictive coding: a theoretical and experimental review
Millidge, B., Seth, A., and Buckley, C. L. (2021) · 2021
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Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · 2016
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Dense associative memory for pattern recognition
Krotov, D. and Hopfield, J. J. (2016) · 2016
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On a model of associative memory with huge storage capacity
Demircigil, M., Heusel, J., Löwe, M., Upgang, S., and Vermet, F. (2017) · 2017
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Gradient episodic memory for continual learning
Lopez-Paz, D. and Ranzato, M. (2017) · 2017
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A review of predictive coding algorithms
Spratling, M. W. (2017) · 2017
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The hippocampus as a predictive map
Stachenfeld, K. L., Botvinick, M. M., and Gershman, S. J. (2017) · 2017
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Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2021) · 2021
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Score-based generative modeling through stochastic differential equations
S., Y., S., J., K., D. P., K., A., E., S., and P., B. (2021) · 2021
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Associative memories via predictive coding
Salvatori, T., Song, Y., Hong, Y., Sha, L., Frieder, S., Xu, Z., Bogacz, R., and Lukasiewicz, T. (2021) · 2021
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2021) · 2021
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Rethinking the hippocampal cognitive map as a meta-learning computational module
Ambrogioni, L. and Ólafsdóttir, H. F. (2023) · 2023
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Effective learning with node perturbation in deep neural networks
Dalm, S., van Gerven, M., and Ahmad, N. (2023) · 2023
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A new frontier for hopfield networks
Krotov, D. (2023) · 2023
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Spontaneous symmetry breaking in generative diffusion models
Raya, G. and Ambrogioni, L. (2023) · 2023
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The neural coding framework for learning generative models
Ororbia, A. and Kifer, D. (2022) · 2064
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