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One major criticism of deep learning centers around the biological implausibility of the credit assignment schema used for learning -- backpropagation of errors.
The representation of the cumulative rounding error of an algorithm as a taylor expansion of the local rounding errors
Linnainmaa, S · 1970
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Maximum likelihood from incomplete data via the EM algorithm
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A local learning algorithm for dynamic feedforward and recurrent networks
Schmidhuber, J · 1989
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Contrastive Hebbian learning in the continuous Hopfield model
Movellan, J. R · 1991
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Credit assignment through time: Alternatives to backpropagation
Bengio, Y., and Frasconi, P · 1993
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The helmholtz machine
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S · 1995
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rao, R. P., and Ballard, D. H · 1999
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Swarm intelligence
Eberhart, R. C., Shi, Y., and Kennedy, J · 2001
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Spike-timing-dependent hebbian plasticity as temporal difference learning
Rao, R. P. N., and Sejnowski, T. J · 2001
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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Evolving neural networks through augmenting topologies
Stanley, K. O., and Miikkulainen, R · 2002
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Learning and inference in the brain
Friston, K · 2003
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Equivalence of backpropagation and contrastive hebbian learning in a layered network
Xie, X., and Seung, H. S · 2003
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A theory of cortical responses
Friston, K · 2005
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Predictive codes for forthcoming perception in the frontal cortex
Summerfield, C., Egner, T., Greene, M., Koechlin, E., Mangels, J., and Hirsch, J · 2006
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How auto-encoders could provide credit assignment in deep networks via target propagation
Bengio, Y · 2014
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Event-driven contrastive divergence for spiking neuromorphic systems
Neftci, E., Das, S., Pedroni, B., Kreutz-Delgado, K., and Cauwenberghs, G · 2014
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Difference target propagation
Lee, D.-H., Zhang, S., Fischer, A., and Bengio, Y · 2015
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Decoupled neural interfaces using synthetic gradients
Jaderberg, M., Czarnecki, W. M., Osindero, S., Vinyals, O., Graves, A., and Kavukcuoglu, K · 2016
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How important is weight symmetry in backpropagation?
Liao, Q., Leibo, J. Z., and Poggio, T · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J · 2016
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Direct feedback alignment provides learning in deep neural networks
Nøkland, A · 2016
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Event-driven random back-propagation: Enabling neuromorphic deep learning machines
Neftci, E. O., Augustine, C., Paul, S., and Detorakis, G · 2017
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Deep learning with dynamic spiking neurons and fixed feedback weights
Samadi, A., Lillicrap, T. P., and Tweed, D. B · 2017
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Error forward-propagation: Reusing feedforward connections to propagate errors in deep learning
Kohan, A. A., Rietman, E. A., and Siegelmann, H. T · 2018
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Feedback alignment in deep convolutional networks
Moskovitz, T. H., Litwin-Kumar, A., and Abbott, L · 2018
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Generalization of equilibrium propagation to vector field dynamics
Scellier, B., Goyal, A., Binas, J., Mesnard, T., and Bengio, Y · 2018
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Biologically motivated algorithms for propagating local target representations
Ororbia, A. G., and Mali, A · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Raissi, M., Perdikaris, P., and Karniadakis, G. E · 2019
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Equivalence of equilibrium propagation and recurrent backpropagation
Scellier, B., and Bengio, Y · 2019
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Theories of error back-propagation in the brain
Whittington, J. C. R., and Bogacz, R · 2019
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Deriving differential target propagation from iterating approximate inverses
Bengio, Y · 2020
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Analog circuits to accelerate the relaxation process in the equilibrium propagation algorithm
Foroushani, A. N., Assaf, H., Noshahr, F. H., Savaria, Y., and Sawan, M · 2020
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Predictive coding approximates backprop along arbitrary computation graphs
Millidge, B., Tschantz, A., and Buckley, C. L · 2022
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Softhebb: Bayesian inference in unsupervised hebbian soft winner-take-all networks
Moraitis, T., Toichkin, D., Journé, A., Chua, Y., and Guo, Q · 2022
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Predictive coding beyond gaussian distributions
Pinchetti, L., Salvatori, T., Yordanov, Y., Millidge, B., Song, Y., and Lukasiewicz, T · 2022
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Ai in health and medicine
Rajpurkar, P., Chen, E., Banerjee, O., and Topol, E. J · 2022
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Learning on arbitrary graph topologies via predictive coding
Salvatori, T., Pinchetti, L., Millidge, B., Song, Y., Bao, T., Bogacz, R., and Lukasiewicz, T · 2022
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Neuromorphic spintronics
Grollier, J., Querlioz, D., Camsari, K., Everschor-Sitte, K., Fukami, S., and Stiles, M. D · 2020
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Direct feedback alignment scales to modern deep learning tasks and architectures
Launay, J., Poli, I., Boniface, F., and Krzakala, F · 2020
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Backpropagation and the brain
Lillicrap, T., Santoro, A., Marris, L., Akerman, C., and Hinton, G · 2020
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A theoretical framework for target propagation
Meulemans, A., Carzaniga, F., Suykens, J., Sacramento, J., and Grewe, B. F · 2020
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Can the brain do backpropagation?—exact implementation of backpropagation in predictive coding networks
Song, Y., Lukasiewicz, T., Xu, Z., and Bogacz, R · 2020
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pypc, 2020
Tschantz, A., and Millidge, B · 2020
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Glsnn: A multi-layer spiking neural network based on global feedback alignment and local stdp plasticity
Zhao, D., Zeng, Y., Zhang, T., Shi, M., and Zhao, F · 2020
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Salvatori, T., Song, Y., Millidge, B., Xu, Z., Sha, L., Emde, C., Bogacz, R., and Lukasiewicz, T · 2022
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Reverse differentiation via predictive coding
Salvatori, T., Song, Y., Xu, Z., Lukasiewicz, T., and Bogacz, R · 2022
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Deep physical neural networks trained with backpropagation
Wright, L. G., Onodera, T., Stein, M. M., Wang, T., Schachter, D. T., Hu, Z., and McMahon, P. L · 2022
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Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation
Zucchet, N., and Sacramento, J · 2022
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Block-local learning with probabilistic latent representations
Kappel, D., Nazeer, K. K., Fokam, C. T., Mayr, C., and Subramoney, A · 2023
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Signal propagation: The framework for learning and inference in a forward pass
Kohan, A., Rietman, E. A., and Siegelmann, H. T · 2023
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Backpropagation at the infinitesimal inference limit of energy-based models: Unifying predictive coding, equilibrium propagation, and contrastive Hebbian learning
Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., and Bogacz, R · 2023
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Forward-forward training of an optical neural network
Oguz, I., Ke, J., Wang, Q., Yang, F., Yildirim, M., Dinc, N. U., Hsieh, J.-L., Moser, C., and Psaltis, D · 2023
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Memristor crossbar circuits implementing equilibrium propagation for on-device learning
Oh, S., An, J., Cho, S., Yoon, R., and Min, K.-S · 2023
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Contrastive-signal-dependent plasticity: Forward-forward learning of spiking neural systems
Ororbia, A · 2023
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Spiking neural predictive coding for continually learning from data streams
Ororbia, A · 2023
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Mortal computation: A foundation for biomimetic intelligence
Ororbia, A., and Friston, K · 2023
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Ororbia, A., and Kelly, M. A · 2023
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The predictive forward-forward algorithm
Ororbia, A., and Mali, A · 2023
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predictive-forward-forward (code-base), 2023
Ororbia, A., and Mali, A · 2023
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Backpropagation-free deep learning with recursive local representation alignment
Ororbia, A. G., Mali, A., Kifer, D., and Giles, C. L · 2023
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Brain-inspired computational intelligence via predictive coding
Salvatori, T., Mali, A., Buckley, C. L., Lukasiewicz, T., Rao, R. P., Friston, K., and Ororbia, A · 2023
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Causal inference via predictive coding
Salvatori, T., Pinchetti, L., M’Charrak, A., Millidge, B., and Lukasiewicz, T · 2023
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Energy-based learning algorithms for analog computing: a comparative study
Scellier, B., Ernoult, M., Kendall, J., and Kumar, S · 2023
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Recurrent predictive coding models for associative memory employing covariance learning
Tang, M., Salvatori, T., Millidge, B., Song, Y., Lukasiewicz, T., and Bogacz, R · 2023
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A robust backpropagation-free framework for images
Zee, T., Ororbia, A., Mali, A., and Nwogu, I · 2023
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Inferring neural activity before plasticity as a foundation for learning beyond backpropagation
Song, Y., Millidge, B., Salvatori, T., Lukasiewicz, T., Xu, Z., and Bogacz, R · 2024
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The neural coding framework for learning generative models
Ororbia, A., and Kifer, D · 2064
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