Theories of Error Back-Propagation in the Brain
Open accessAbstract
This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks. Computational models implementing these theories achieve learning as efficient as artificial neural networks, but they use simple synaptic plasticity rules based on activity of presynaptic and postsynaptic neurons. The models have similarities, such as including both feedforward and feedback connections, allowing information about error to propagate throughout the network. Furthermore, they incorporate experimental evidence on neural connectivity, responses, and plasticity. These models provide insights on how brain networks might be organised such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Backpropagation's core assumptions, such as a global error signal and strict separation of inference and learning phases, are incompatible with current knowledge of cortical computations and introduce interference.
“Yet its core assumptions, like a global error signal and strict separation of inference and learning phases, are incompatible with both current knowledge of cortical computations [Whittington and Bogacz, 2019] and introduce interference [Song et al., 2024].”
From Closed-form predictive coding via hierarchical Gaussian filters (2026) · cites this paper as Whittington and Bogacz, 2019
The ‘Lack of Local Error Representation’ section says conventional back-propagation relies on externally computed errors, making its implementation through local biological synaptic signals unclear. The ‘Temporal-Error Models’ section also questions the biological plausibility of a global signal distinguishing learning phases, but this concerns a contrastive-learning model; the paper does not present a global error signal or strict phase separation as core assumptions of back-propagation itself. Its concerns are more qualified than the claim that these assumptions are incompatible with cortical computations. The interference claim is attached to Song et al., not this source. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · see citing works
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918 · see citing works
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924 · cited 1× · see citing works
“Further details are discussed in several excellent reviews [52 – 55].”
BackgroundRao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · see citing works
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744 · see citing works
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402 · see citing works
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · see citing works
Synaptic plasticity: taming the beast
Abbott, L. F., & Nelson, S. B. (2000) · Nature Neuroscience · doi:10.1038/81453 · Cited by 2,263 · cited 1× · see citing works
“Such a form of plasticity may exist in a several types of synapse in the brain [61].”
BackgroundUsing goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · see citing works
Highly nonrandom features of synaptic connectivity in local cortical circuits
Song, S., Sjöström, P. J., Reigl, M., Nelson, S., & Chklovskii, D. B. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030068 · Cited by 1,512 · see citing works
Competitive learning: From interactive activation to adaptive resonance
Grossberg, S. (1987) · Cognitive Science · doi:10.1111/j.1551-6708.1987.tb00862.x · Cited by 1,471 · see citing works
A new cellular mechanism for coupling inputs arriving at different cortical layers
Larkum, M. E., Zhu, J. J., & Sakmann, B. (1999) · Nature · doi:10.1038/18686 · Cited by 1,180 · see citing works
How Do Expectations Shape Perception?
de Lange, F. P., Heilbron, M., & Kok, P. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.06.002 · Cited by 975 · see citing works
Generalization of back-propagation to recurrent neural networks
Pineda, F. J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.2229 · Cited by 931 · see citing works
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T., Cownden, D., Tweed, D., & Akerman, C. J. (2016) · Nature Communications · doi:10.1038/ncomms13276 · Cited by 879 · see citing works
Expectation in perceptual decision making: neural and computational mechanisms
Summerfield, C., & de Lange, F. P. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3838 · Cited by 832 · see citing works
Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks
Vogels, T. P., Sprekeler, H., Zenke, F., Claudia Clopath, A., & Gerstner, W. (2011) · Science · doi:10.1126/science.1211095 · Cited by 779 · see citing works
Disynaptic Inhibition between Neocortical Pyramidal Cells Mediated by Martinotti Cells
Silberberg, G., & Markram, H. (2007) · Neuron · doi:10.1016/j.neuron.2007.02.012 · Cited by 768 · see citing works
The recent excitement about neural networks
Crick, F. (1989) · Nature · doi:10.1038/337129a0 · Cited by 724 · see citing works
Neural repetition suppression reflects fulfilled perceptual expectations
Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M.-M., & Egner, T. (2008) · Nature Neuroscience · doi:10.1038/nn.2163 · Cited by 692 · see citing works
Vector-based navigation using grid-like representations in artificial agents
Banino, A., Barry, C., Uría, B., Blundell, C., Lillicrap, T., Mirowski, P., Pritzel, A., Chadwick, M. J., Degris, T., Modayil, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0102-6 · Cited by 682 · see citing works
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
Zenke, F., & Ganguli, S. (2018) · Neural Computation · doi:10.1162/neco_a_01086 · Cited by 533 · see citing works
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation
Scellier, B., & Bengio, Y. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00024 · Cited by 504 · see citing works
A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions
Leinweber, M., Ward, D. R., Sobczak, J. M., Attinger, A., & Keller, G. B. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.036 · Cited by 375 · see citing works
Towards deep learning with segregated dendrites
Guerguiev, J., Lillicrap, T., & Richards, B. A. (2017) · eLife · doi:10.7554/elife.22901 · Cited by 373 · see citing works
O'Reilly, R. C. (1996) · Neural Computation · doi:10.1162/neco.1996.8.5.895 · Cited by 352 · see citing works
Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
Seung, H. S. (2003) · Neuron · doi:10.1016/s0896-6273(03)00761-x · Cited by 340 · see citing works
A tutorial on the free-energy framework for modelling perception and learning
Bogacz, R. (2017) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2015.11.003 · Cited by 283 · see citing works
Self-citationHopfield, J. J. (1988) · National Academy of Sciences eBooks · Cited by 279 · see citing works
Whittington, J. C. R., & Bogacz, R. (2017) · Neural Computation · doi:10.1162/neco_a_00949 · Cited by 278 · see citing works
Self-citationVisuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex
Attinger, A., Wang, B., & Keller, G. B. (2017) · Cell · doi:10.1016/j.cell.2017.05.023 · Cited by 252 · see citing works
Control of synaptic plasticity in deep cortical networks
Roelfsema, P. R., & Holtmaat, A. J. D. G. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2018.6 · Cited by 245 · see citing works
An Introduction to Model-Based Cognitive Neuroscience
Forstmann, B. U., & Wagenmakers, E. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2236-9 · Cited by 228 · see citing works
A more biologically plausible learning rule for neural networks.
Mazzoni, P., Andersen, R. A., & Jordan, M. I. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.10.4433 · Cited by 227 · see citing works
Pike, F. G., Meredith, R. M., Olding, A. W. A., & Paulsen, O. (1999) · The Journal of Physiology · doi:10.1111/j.1469-7793.1999.0571p.x · Cited by 226 · see citing works
Lee, D., Zhang, S., Fischer, A., & Bengio, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23528-8_31 · Cited by 225 · see citing works
Untangling GABAergic wiring in the cortical microcircuit
Kubota, Y. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.10.003 · Cited by 172 · see citing works
Unsupervised learning by competing hidden units
Krotov, D., & Hopfield, J. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820458116 · Cited by 165 · see citing works
Supervised Learning in Multilayer Spiking Neural Networks
Sporea, I., & Grüning, A. (2012) · Neural Computation · doi:10.1162/neco_a_00396 · Cited by 159 · see citing works
Woloszyn, L., & Sheinberg, D. L. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.032 · Cited by 149 · see citing works
Dendritic solutions to the credit assignment problem
Richards, B. A., & Lillicrap, T. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.08.003 · Cited by 148 · see citing works
Learning with three factors: modulating Hebbian plasticity with errors
Kuśmierz, Ł., Isomura, T., & Toyoizumi, T. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.020 · Cited by 145 · see citing works
How Important Is Weight Symmetry in Backpropagation?
Liao, Q., Leibo, J. Z., & Poggio, T. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10279 · Cited by 136 · see citing works
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
Alopex: A Correlation-Based Learning Algorithm for Feedforward and Recurrent Neural Networks
Unnikrishnan, K. P., & Venugopal, K. P. (1994) · Neural Computation · doi:10.1162/neco.1994.6.3.469 · Cited by 113 · see citing works
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks
Werfel, J., Xie, X. S., & Seung, H. S. (2005) · Neural Computation · doi:10.1162/089976605774320539 · Cited by 112 · see citing works
Deep Supervised Learning Using Local Errors
Mostafa, H., Ramesh, V., & Cauwenberghs, G. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00608 · Cited by 110 · see citing works
STDP-Compatible Approximation of Backpropagation in an Energy-Based Model
Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., & Wu, Y. (2017) · Neural Computation · doi:10.1162/neco_a_00934 · Cited by 108 · see citing works
Learning with two sites of synaptic integration
Kording, K. P., & Peter König, B. (2000) · Network Computation in Neural Systems · doi:10.1088/0954-898x_11_1_302 · Cited by 105 · see citing works
Supervised and Unsupervised Learning with Two Sites of Synaptic Integration
Kording, K. P., & Peter König, B. (2001) · Journal of Computational Neuroscience · doi:10.1023/a:1013776130161 · Cited by 98 · see citing works
Predictive Coding in Sensory Cortex
Kok, P., & de Lange, F. P. (2015) · An Introduction to Model-Based Cognitive Neuroscience · doi:10.1007/978-1-4939-2236-9_11 · Cited by 98 · see citing works
A theory of local learning, the learning channel, and the optimality of backpropagation
Baldi, P., & Sadowski, P. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.07.006 · Cited by 80 · see citing works
Somato-dendritic synaptic plasticity and error-backpropagation in active dendrites
Schiess, M., Urbanczik, R., & Senn, W. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004638 · Cited by 75 · see citing works
Sensory cortex is optimized for prediction of future input
Singer, Y., Teramoto, Y., Willmore, B. D. B., King, A. J., Schnupp, J. W. H., & Harper, N. S. (2018) · eLife · doi:10.7554/elife.31557 · Cited by 71 · see citing works
Theta Coordinated Error-Driven Learning in the Hippocampus
Ketz, N., Morkonda, S. G., & O’Reilly, R. C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003067 · Cited by 69 · see citing works
Contrastive Learning and Neural Oscillations
Baldi, P., & Pineda, F. J. (1991) · Neural Computation · doi:10.1162/neco.1991.3.4.526 · Cited by 60 · see citing works
Parallel Distributed Processing Theory in the Age of Deep Networks
Bowers, J. S. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.09.013 · Cited by 39 · see citing works
Active Inference and Learning in the Cerebellum
Friston, K. J., & Herreros, I. (2016) · Neural Computation · doi:10.1162/neco_a_00863 · Cited by 37 · see citing works
Equivalence of Equilibrium Propagation and Recurrent Backpropagation
Scellier, B., & Bengio, Y. (2018) · Neural Computation · doi:10.1162/neco_a_01160 · Cited by 35 · see citing works
Kickback Cuts Backprop's Red-Tape: Biologically Plausible Credit Assignment in Neural Networks
Balduzzi, D., Vanchinathan, H. P., & Buhmann, J. M. (2015) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v29i1.9217 · Cited by 29 · see citing works
Flagged first, then by citations
6 of the 73 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- [6]Whittington, J.C.R. et al. (2018) Generalisation of structural knowledge in the hippocampal-entorhinal system. In 31st Conference on Neural Information Processing Systems (NIPS 2018), Montreal
- [13]Sacramento, J. et al. (2018) Dendritic cortical microcircuits approximate the backpropagation algorithm. In 31st Conference on Neural Information Processing Systems (NIPS 2018), Montreal
- [25]Scellier, B. et al. (2018) Generalization of equilibrium propagation to vector field dynamics. arXiv 1808.04873 · arXiv
- [28]Bartunov, S. et al. (2018) Assessing the scalability of biologically-motivated deep learning algorithms and architectures. In 31st Conference on Neural Information Processing Systems (NIPS 2018), Montreal
- [35]Bengio, Y. (2014) How auto-encoders could provide credit assignment in deep networks via target propagation. arXiv preprint arXiv:1407.7906 · arXiv
- [41]Ororbia, A.G. and Mali, A. (2018) Biologically motivated algorithms for propagating local target representations. arXiv preprint arXiv:1805.11703 · arXiv
- [58]O’Reilly R.C. Munakata Y. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain 2000 MIT Press
Cited by
Sorted by citations
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0277-3 · Cited by 908 · see citing works
Synaptic Plasticity Forms and Functions
Magee, J. C., & Grienberger, C. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-090919-022842 · Cited by 806 · see citing works
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
Lindsay, G. W. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01544 · Cited by 571 · see citing works
Development and arealization of the cerebral cortex
Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.009 · Cited by 408 · see citing works
A review of learning in biologically plausible spiking neural networks
Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L., & McGinnity, T. M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.036 · Cited by 424 · see citing works
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks
Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387 · see citing works
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Payeur, A., Guerguiev, J., Zenke, F., Richards, B. A., & Naud, R. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00857-x · Cited by 286 · see citing works
Deep reinforcement learning and its neuroscientific implications
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269 · see citing works
Supervised learning in spiking neural networks: A review of algorithms and evaluations
Wang, X., Lin, X., & Dang, X. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.02.011 · Cited by 276 · see citing works
Spine dynamics in the brain, mental disorders and artificial neural networks
Kasai, H., Ziv, N. E., Okazaki, H., Yagishita, S., & Toyoizumi, T. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00467-3 · Cited by 180 · see citing works
Semedo, J. D., Jasper, A. I., Zandvakili, A., Krishna, A., Aschner, A., Machens, C. K., Kohn, A., & Yu, B. M. (2022) · Nature Communications · doi:10.1038/s41467-022-28552-w · Cited by 152 · see citing works
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
Watson, D. (2019) · Minds and Machines · doi:10.1007/s11023-019-09506-6 · Cited by 148 · see citing works
Nonmonotonic Plasticity: How Memory Retrieval Drives Learning
Ritvo, V. J. H., Turk‐Browne, N. B., & Norman, K. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.06.007 · Cited by 146 · see citing works
Macpherson, T., Churchland, A. K., Sejnowski, T., DiCarlo, J. J., Kamitani, Y., Takahashi, H., & Hikida, T. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.09.018 · Cited by 157 · see citing works
Review and Classification of Bio-inspired Algorithms and Their Applications
Fan, X., Sayers, W., Zhang, S., Han, Z., Ren, L., & Chizari, H. (2020) · Journal of Bionic Engineering · doi:10.1007/s42235-020-0049-9 · Cited by 142 · see citing works
Memristive and CMOS Devices for Neuromorphic Computing
Milo, V., Malavena, G., Compagnoni, C. M., & Ielmini, D. (2020) · Materials · doi:10.3390/ma13010166 · Cited by 140 · see citing works
Vezoli, J., Magrou, L., Goebel, R., Wang, X., Knoblauch, K., Vinck, M. A., & Kennedy, H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117479 · Cited by 125 · see citing works
Brain-inspired learning in artificial neural networks: A review
Schmidgall, S., Ziaei, R., Achterberg, J., Kirsch, L., Hajiseyedrazi, S. P., & Eshraghian, J. K. (2024) · APL Machine Learning · doi:10.1063/5.0186054 · Cited by 143 · see citing works
Language ERPs reflect learning through prediction error propagation
Fitz, H., & Chang, F. (2019) · Cognitive Psychology · doi:10.1016/j.cogpsych.2019.03.002 · Cited by 117 · see citing works
Where is the error? Hierarchical predictive coding through dendritic error computation
Mikulasch, F. A., Rudelt, L., Wibral, M., & Priesemann, V. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.09.007 · Cited by 128 · see citing works
Deep active inference as variational policy gradients
Millidge, B. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102348 · Cited by 115 · see citing works
Biologically plausible deep learning — But how far can we go with shallow networks?
Illing, B., Gerstner, W., & Brea, J. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.06.001 · Cited by 107 · see citing works
Caligiore, D., Arbib, M. A., Miall, R. C., & Baldassarre, G. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.02.008 · Cited by 105 · see citing works
Nakajima, M., Inoue, K., Tanaka, K., Kuniyoshi, Y., Hashimoto, T., & Nakajima, K. (2022) · Nature Communications · doi:10.1038/s41467-022-35216-2 · Cited by 108 · see citing works
Deep learning in electron microscopy
Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 105 · see citing works
A Layered Spiking Neural System for Classification Problems
Zhang, G., Zhang, X., Rong, H., Paul, P., Zhu, M. Q., Neri, F., & Ong, Y.-S. (2022) · International Journal of Neural Systems · doi:10.1142/s012906572250023x · Cited by 103 · see citing works
Going in circles is the way forward: the role of recurrence in visual inference
van Bergen, R. S., & Kriegeskorte, N. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.11.009 · Cited by 93 · see citing works
Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs
Millidge, B., Tschantz, A., & Buckley, C. L. (2022) · Neural Computation · doi:10.1162/neco_a_01497 · Cited by 95 · see citing works
Yang, S., & Chen, B. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3329525 · Cited by 93 · see citing works
A generative model of memory construction and consolidation
Spens, E., & Burgess, N. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01799-z · Cited by 101 · see citing works
How Outcome Uncertainty Mediates Attention, Learning, and Decision-Making
Monosov, I. E. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.009 · Cited by 87 · see citing works
The mediodorsal thalamus in executive control
Wolff, M., & Halassa, M. M. (2024) · Neuron · doi:10.1016/j.neuron.2024.01.002 · Cited by 96 · see citing works
Inferring neural activity before plasticity as a foundation for learning beyond backpropagation
Song, Y., Millidge, B., Salvatori, T., Lukasiewicz, T., Xu, Z., & Bogacz, R. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01514-1 · Cited by 85 · see citing works
Self-citationArtificial intelligence for clinical decision support in neurology
Pedersen, M., Verspoor, K. M., Jenkinson, M., Law, M., Abbott, D. F., & Jackson, G. D. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa096 · Cited by 89 · see citing works
How deep is the brain? The shallow brain hypothesis
Suzuki, M., Pennartz, C. M. A., & Aru, J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00756-z · Cited by 79 · see citing works
Feng, L., Dai, H., Song, X. H., Liu, J., & Mei, X. (2021) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2021.130986 · Cited by 76 · see citing works
Zhang, C., Chen, X., Wang, S., Hu, J., Wang, C., & Liu, X. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2021.114261 · Cited by 76 · see citing works
A parameter-free statistical test for neuronal responsiveness
Montijn, J. S., Seignette, K., Howlett, M. H. C., Cazemier, J. L., Kamermans, M., Levelt, C. N., & Heimel, J. A. (2021) · eLife · doi:10.7554/elife.71969 · Cited by 74 · see citing works
Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat
Arseniev‐Koehler, A., & Foster, J. G. (2022) · Sociological Methods & Research · doi:10.1177/00491241221122603 · Cited by 74 · see citing works
The plasticitome of cortical interneurons
McFarlan, A. R., Chou, C. Y. C., Watanabe, A., Cherepacha, N., Haddad, M. G., Owens, H., & Sjöström, P. J. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00663-9 · Cited by 67 · see citing works
Jiang, H., Liu, T., He, P., & Chen, Q. (2020) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.127816 · Cited by 71 · see citing works
A theory of learning to infer.
Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020) · Psychological Review · doi:10.1037/rev0000178 · Cited by 70 · see citing works
Experimental validation of the free-energy principle with in vitro neural networks
Isomura, T., Kotani, K., Jimbo, Y., & Friston, K. J. (2023) · Nature Communications · doi:10.1038/s41467-023-40141-z · Cited by 71 · see citing works
Spike-driven multi-scale learning with hybrid mechanisms of spiking dendrites
Yang, S., Pang, Y., Wang, H., Lei, T., Pan, J., Wang, J., & Jin, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126240 · Cited by 64 · see citing works
Prediction and optimization of oscillating wave surge converter using machine learning techniques
Liu, Z., Wang, Y., & Hua, X. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.112677 · Cited by 63 · see citing works
The neural mechanisms of face processing: cells, areas, networks, and models
Freiwald, W. A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.12.007 · Cited by 62 · see citing works
A predictive coding model of the N400
Eddine, S. N., Brothers, T. A., Wang, L., Spratling, M., & Kuperberg, G. R. (2024) · Cognition · doi:10.1016/j.cognition.2024.105755 · Cited by 62 · see citing works
Data Processing Using Artificial Neural Networks
Alaloul, W. S., & Qureshi, A. H. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.91935 · Cited by 61 · see citing works
Tosun, M. (2021) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-021-01018-x · Cited by 60 · see citing works
Song, Y., Lukasiewicz, T., Xu, Z., & Bogacz, R. (2020) · PubMed · Cited by 60 · see citing works
Self-citationLaminar-specific cortico-cortical loops in mouse visual cortex
Young, H., Belbut, B., Baeta, M., & Petreanu, L. (2021) · eLife · doi:10.7554/elife.59551 · Cited by 59 · see citing works
Learning excitatory-inhibitory neuronal assemblies in recurrent networks
Mackwood, O., Naumann, L., & Sprekeler, H. (2021) · eLife · doi:10.7554/elife.59715 · Cited by 64 · see citing works
Ding, N., Guo, C., Li, C., Zhou, Y., & Chai, X. (2021) · BioMed Research International · doi:10.1155/2021/6638919 · Cited by 53 · see citing works
Unveiling functions of the visual cortex using task-specific deep neural networks
Dwivedi, K., Bonner, M. F., Cichy, R. M., & Roig, G. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009267 · Cited by 52 · see citing works
Hybrid predictive coding: Inferring, fast and slow
Tscshantz, A., Millidge, B., Seth, A. K., & Buckley, C. L. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011280 · Cited by 51 · see citing works
Asymmetric reinforcement learning facilitates human inference of transitive relations
Ciranka, S., Domingo, J. L., Padezhki, I., Wicharz, C., Wu, C. M., & Spitzer, B. (2022) · Nature Human Behaviour · doi:10.1038/s41562-021-01263-w · Cited by 51 · see citing works
Predictive processing in cognitive robotics: A review
Ciria, A., Schillaci, G., Pezzulo, G., Hafner, V. V., & Lara, B. (2021) · Neural Computation · doi:10.1162/neco_a_01383 · Cited by 51 · see citing works
Advances of machine learning-assisted small extracellular vesicles detection strategy
Zhang, Q., Ren, T.-J., Cao, K., & Xu, Z. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116076 · Cited by 50 · see citing works
Deep Predictive Learning in Neocortex and Pulvinar
O’Reilly, R. C., Russin, J., Zolfaghar, M., & Rohrlich, J. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01708 · Cited by 43 · see citing works
Gillon, C. J., Pina, J. E., Lecoq, J. A., Ahmed, R., Billeh, Y. N., Caldejon, S., Groblewski, P. A., Henley, T. M., Kato, I., Lee, E., et al. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1009-23.2023 · Cited by 61 · see citing works
Predictive coding across the left fronto-temporal hierarchy during language comprehension
Wang, L., Schoot, L., Brothers, T. A., Alexander, E., Warnke, L., Kim, M. J., Khan, S., Hämäläinen, M., & Kuperberg, G. R. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac356 · Cited by 49 · see citing works
A Network Perspective on Sensorimotor Learning
Sohn, H., Meirhaeghe, N., Rajalingham, R., & Jazayeri, M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.11.007 · Cited by 44 · see citing works
Dendritic Computing: Branching Deeper into Machine Learning
Acharya, J., Basu, A., Legenstein, R. A., Limbacher, T., Poirazi, P., & Wu, X. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.10.001 · Cited by 47 · see citing works
Levels of biological plausibility
Love, B. C. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0632 · Cited by 46 · see citing works
Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory unitsPreprint
Cornford, J. H., Kalajdzievski, D., Leite, M., Lamarquette, A., Kullmann, D. M., & Richards, B. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.02.364968 · Cited by 43 · see citing works
Training End-to-End Analog Neural Networks with Equilibrium PropagationPreprint
Kendall, J. D., Pantone, R. D., Manickavasagam, K., Bengio, Y., & Scellier, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01981 · Cited by 55 · see citing works
Deng, T., Duan, H.-F., & Keramat, A. (2022) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2022.2035257 · Cited by 41 · see citing works
Sakemi, Y., Morino, K., Morie, T., & Aihara, K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3095068 · Cited by 41 · see citing works
Zhang, H., Zhang, R., He, Q., & Liu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3072648 · Cited by 38 · see citing works
Representational drift as a window into neural and behavioural plasticity
Micou, C., & O’Leary, T. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102746 · Cited by 36 · see citing works
Yang, S., Wang, H., Pang, Y., Jin, Y., & Linares-Barranco, B. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3327142 · Cited by 36 · see citing works
Mari, T., Henderson, J., Maden, M., Nevitt, S. J., Duarte, R. V., & Fallon, N. B. (2021) · Journal of Pain · doi:10.1016/j.jpain.2021.07.011 · Cited by 36 · see citing works
Small, correlated changes in synaptic connectivity may facilitate rapid motor learning
Feulner, B., Perich, M. G., Chowdhury, R. H., Miller, L. E., Gallego, J. Á., & Claudia Clopath, A. (2022) · Nature Communications · doi:10.1038/s41467-022-32646-w · Cited by 38 · see citing works
Molecular Computation for Molecular Classification
Kieffer, C., Genot, A. J., Rondelez, Y., & Gines, G. (2023) · Advanced Biology · doi:10.1002/adbi.202200203 · Cited by 35 · see citing works
The N400 in silico: A review of computational models
Eddine, S. N., Brothers, T. A., & Kuperberg, G. R. (2022) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2022.03.005 · Cited by 32 · see citing works
Vaila, R., Chiasson, J., & Saxena, V. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.3035164 · Cited by 32 · see citing works
Introducing principles of synaptic integration in the optimization of deep neural networks
Dellaferrera, G., Woźniak, S., Indiveri, G., Pantazi, A., & Eleftheriou, E. S. (2022) · Nature Communications · doi:10.1038/s41467-022-29491-2 · Cited by 31 · see citing works
Recurrent predictive coding models for associative memory employing covariance learning
Tang, M., Salvatori, T., Millidge, B., Song, Y., Lukasiewicz, T., & Bogacz, R. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010719 · Cited by 31 · see citing works
Self-citationNeuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation
Contreras, L. F. H., Truong, N. D., Eshraghian, J. K., Xu, Z., Huang, Z., Bersani--Veroni, T., Aguilar, I., Leung, W. H., Nikpour, A., & Kavehei, O. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae488 · Cited by 30 · see citing works
Evolving interpretable plasticity for spiking networks
Jordan, J., Schmidt, M., Senn, W., & Petrovici, M. A. (2021) · eLife · doi:10.7554/elife.66273 · Cited by 30 · see citing works
Multilevel development of cognitive abilities in an artificial neural network
Volzhenin, K., Changeux, J., & Dumas, G. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201304119 · Cited by 30 · see citing works
Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions
Price, B. H., & Gavornik, J. P. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.929348 · Cited by 29 · see citing works
Mumuni, F., & Mumuni, A. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101188 · Cited by 28 · see citing works
Ayyıldız, E., Erdoğan, M., & Taskin, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105029 · Cited by 28 · see citing works
Bidirectional flow of action potentials in axons drives activity dynamics in neuronal cultures
Mateus, J. C., Lopes, C. D. F., Aroso, M., Costa, A. R., Gerós, A., Meneses, J., Pascoal‐Faria, P., Neto, E., Lamghari, M., Sousa, M. M., et al. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac41db · Cited by 27 · see citing works
Learning cortical representations through perturbed and adversarial dreaming
Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2022) · eLife · doi:10.7554/elife.76384 · Cited by 28 · see citing works
Optimal plasticity for memory maintenance during ongoing synaptic change
Raman, D. V., & O’Leary, T. (2021) · eLife · doi:10.7554/elife.62912 · Cited by 27 · see citing works
Spiking Neural Networks for Visual Place Recognition Via Weighted Neuronal Assignments
Hussaini, S., Milford, M. J., & Fischer, T. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3149030 · Cited by 27 · see citing works
Traffic safety assessment and prediction under different lighting service states in road tunnels
Chen, J., You, L., Yang, M., & Wang, X. (2023) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2023.105001 · Cited by 26 · see citing works
Omission responses in local field potentials in rat auditory cortex
Auksztulewicz, R., Rajendran, V. G., Peng, F., Schnupp, J. W. H., & Harper, N. S. (2023) · BMC Biology · doi:10.1186/s12915-023-01592-4 · Cited by 26 · see citing works
Cone, I., & Shouval, H. Z. (2021) · eLife · doi:10.7554/elife.63751 · Cited by 25 · see citing works
Multilayer extreme learning machine: a systematic review
Kaur, R., Roul, R. K., & Batra, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14634-4 · Cited by 25 · see citing works
Network properties determine neural network performance
Jiang, C., Huang, Z., Pedapati, T., Chen, P., Sun, Y., & Gao, J. (2024) · Nature Communications · doi:10.1038/s41467-024-48069-8 · Cited by 24 · see citing works
Correcting the hebbian mistake: Toward a fully error-driven hippocampus
Zheng, Y., Liu, X. L., Nishiyama, S., Charan Ranganath, A., & O’Reilly, R. C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010589 · Cited by 24 · see citing works
Toward robust and scalable deep spiking reinforcement learning
Akl, M., Ergene, D., Walter, F., & Knoll, A. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1075647 · Cited by 24 · see citing works
The study of plasticity has always been about gradients
Richards, B. A., & Kording, K. P. (2023) · The Journal of Physiology · doi:10.1113/jp282747 · Cited by 24 · see citing works
Learning by non-interfering feedback chemical signaling in physical networks
Anisetti, V. R., Scellier, B., & Schwarz, J. M. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023024 · Cited by 24 · see citing works
Equilibrium Propagation for Memristor-Based Recurrent Neural Networks
Zoppo, G., Marrone, F., & Corinto, F. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00240 · Cited by 24 · see citing works
A rapid and efficient learning rule for biological neural circuitsPreprint
Sezener, E., Grabska‐Barwińska, A., Kostadinov, D., Beau, M., Krishnagopal, S., Budden, D. M., Hütter, M., Veness, J., Botvinick, M., Claudia Clopath, A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.10.434756 · Cited by 24 · see citing works
Unitary learning for diffractive deep neural network
Xiao, Y.-L., Li, S., Situ, G., & You, Z. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2020.106499 · Cited by 24 · see citing works
- Authors
- Whittington, J. C. R., & Bogacz, R.
- Affiliations
- University of Oxford (GB)
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 23
- Issue
- 3
- Pages
- 235-250
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC6382460
- OpenAlex
- W2913907987
- Cited by
- 420
- References
- 73
- Citations by year
- 2026: 34 · 2025: 69 · 2024: 53 · 2023: 72 · 2022: 61 · 2021: 65 · 2020: 52 · 2019: 14
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Trends in Cognitive Sciences (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (MC_UU_12024/5)
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- error back-propagation, brain-inspired learning, synaptic plasticity, neural circuits, computational models of learning, feedback connections, cortical hierarchy, neuronal connectivity, artificial neural networks
- MeSH terms
- Brain, Humans, Models, Theoretical, Nerve Net, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
