Theories of Error Back-Propagation in the Brain

Cited by (420)

Items 101–200 of 420

1. A Brief Taxonomy of Hybrid Intelligence
   Niccolò Pescetelli · 2021 · Forecasting
2. Learning cortical representations through perturbed and adversarial dreaming.
   Nicolas Deperrois, Mihai Alexandru Petrovici, Walter Senn, Jakob Jordan · 2022
3. Testable or bust: theoretical lessons for predictive processing
   Marcin Miłkowski, Piotr Litwin · 2022 · Synthese
4. Photons guided by axons may enable backpropagation-based learning in the brain
   Parisa Zarkeshian, Taylor Kergan, Roohollah Ghobadi, Wilten Nicola, Christoph Simon · 2022 · Scientific Reports
5. A Novel Fast Feedforward Neural Networks Training Algorithm
   Jarosław Bilski, Bartosz Kowalczyk, Andrzej Marjański, Michał Gandor, Jacek M. Żurada · 2021 · Journal of Artificial Intelligence and Soft Computing Research
6. Modeling the Ventral and Dorsal Cortical Visual Pathways Using Artificial Neural Networks
   Zhixian Han, Anne B. Sereno · 2021 · Neural Computation
7. A neuronal least-action principle for real-time learning in cortical circuits
   Walter Senn, Dominik Dold, Ákos F. Kungl, Benjamin Ellenberger, Jakob Jordan, Yoshua Bengio, João Sacramento, Mihai Alexandru Petrovici · 2024 · eLife
8. A novel structural damage detection method using a hybrid IDE–BP model
   Jiangtao Mei, Lei Wu, Erqi Chen, Wensheng Xiao, Liang Zhong, Jingjing Guo, Wentao Li · 2023 · Knowledge-Based Systems
9. An efficient cybersecurity framework for facial video forensics detection based on multimodal deep learning
   Ahmed Sedik, Osama S. Faragallah, Hala S. El‐sayed, Ghada M. El‐Banby, Fathi E. Abd El‐Samie, Ashraf A. M. Khalaf, Walid El‐Shafai · 2021 · Neural Computing and Applications
10. Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks
    Philipp Weidel, Renato Duarte, Abigail Morrison · 2021 · Frontiers in Computational Neuroscience
11. On the relationship between predictive coding and backpropagation
    Robert Rosenbaum · 2022 · PLoS ONE
12. Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
    Yushun Tang, Ce Zhang, Heng Xu, Shuoshuo Chen, Jie Cheng, Luziwei Leng, Qinghai Guo, Zhihai He · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
13. Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning
    M. Amin Khajehnejad, Forough Habibollahi, Alon Loeffler, Aswin Paul, Adeel Razi, Brett J. Kagan · 2025 · Cyborg and Bionic Systems
14. A deep transfer regression method based on seed replacement considering balanced domain adaptation
    Teng Zhang, Hao Sun, Fangyu Peng, Shengqiang Zhao, Rong Yan · 2022 · Engineering Applications of Artificial Intelligence
15. Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants
    Hagar Fathi Gouda, Fardos A.M. Hassan, Eman E. El-Araby, Sherif A. Moawed · 2022 · BMC Veterinary Research
16. Meta-learning biologically plausible plasticity rules with random feedback pathways
    Navid Shervani-Tabar, Robert Rosenbaum · 2023 · Nature Communications
17. Change detection in the primate auditory cortex through feedback of prediction error signals
    Keitaro Obara, Teppei Ebina, Shin-Ichiro Terada, Takanori Uka, Misako Komatsu, Masafumi Takaji, Akiya Watakabe, Kenta Kobayashi, Yoshito Masamizu, Hiroaki Mizukami et al. · 2023 · Nature Communications
18. Mechanism and data hybrid-driven cutting forces prediction model for end milling
    Chang Ni, Jixiang Yang, Han Ding · 2025 · Journal of Intelligent Manufacturing
19. Deep learning networks with rough-refinement optimization for food quality assessment
    Jin Zhou, Kang Zhou, Gexiang Zhang, Qiyu Liu, Wangyang Shen, Weiping Jin · 2022 · Natural Computing
20. Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network
    Ikhwan Jeon, Taegon Kim · 2023 · Frontiers in Computational Neuroscience
21. Theoretical investigation of an array of Josephson junction neuron circuits actuating a mechanical leg and the array in mimicking a multi-legged locomotion
    Isidore Komofor Ngongiah, Gayathri Vivekanandan, Gaetan Fautso Kuiate, F. C. Fobasso Mbognou, Karthikeyan Rajagopal · 2023 · Pramana
22. Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks
    Naresh Balaji Ravichandran, Anders Lansner, Pawel Andrzej Herman · 2025 · Neurocomputing
23. Local Memory Allocation Recruits Memory Ensembles Across Brain Regions
    Ayal Lavi, Megha Sehgal, André F. de Sousa, Donara Ter-Mkrtchyan, Fardad Sisan, Alessandro Luchetti, Anna Okabe, Cameron Bear, Alcino J. Silva · 2022 · Neuron
24. Learning representations in Bayesian Confidence Propagation neural networks
    Naresh Balaji Ravichandran, Anders Lansner, Pawel Andrzej Herman · 2020 · International Joint Conference on Neural Networks (IJCNN)
25. Reverse Differentiation via Predictive Coding
    Tommaso Salvatori, Yuhang Song, Zhenghua Xu, Thomas Lukasiewicz, Rafał Bogacz · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
26. HyperMAML: Few-shot adaptation of deep models with hypernetworks
    Marcin Przewięźlikowski, Przemysław Przybysz, Jacek Tabor, Maciej Zięba, Przemysław Spurek · 2024 · Neurocomputing
27. Asymmetrical estimator for training encapsulated deep photonic neural networks
    Yizhi Wang, Minjia Chen, Chunhui Yao, Jie Ma, Ting Yan, Richard V. Penty, Qixiang Cheng · 2025 · Nature Communications
28. A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
    Zong Ke, Jingyun Xu, Zizhou Zhang, Yu Cheng, Wenjun Wu · 2024 · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
29. Multi-volume modeling of Eucalyptus trees using regression and artificial neural networks
    Gileno Brito de Azevedo, Heitor Vicensotto Tomiazzi, Glauce Táıs de Oliveira Sousa Azevedo, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Marcos Talvani Pereira de Souza, Tays Silva Batista, Humberto de Jesus Eufrade-Junior, Saulo Philipe Sebastião Guerra · 2020 · PLoS ONE
30. Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks
    Salvatori, Tommaso, Song, Yuhang, Lukasiewicz, Thomas, Bogacz, Rafal, Xu, Zhenghua · 2021 · arXiv
31. A Review of Neuroscience-Inspired Machine Learning
    Alexander G. Ororbia, Ankur Mali, Adam Kohan, Beren Millidge, Tommaso Salvatori · 2024
32. Learning on Arbitrary Graph Topologies via Predictive Coding
    Tommaso Salvatori, Luca Pinchetti, Beren Millidge, Yuhang Song, Tian-Yi Bao, Rafał Bogacz, Thomas Lukasiewicz · 2022 · PubMed
33. Cerebellar contributions across behavioural timescales: a review from the perspective of cerebro-cerebellar interactions
    Ellen Boven, Nadia L. Cerminara · 2023 · Frontiers in Systems Neuroscience
34. Hierarchical temporal prediction captures motion processing along the visual pathway
    Yosef Singer, Luke Taylor, Ben D. B. Willmore, Andrew J. King, Nicol Spencer Harper · 2023 · eLife
35. Predictive coding with spiking neurons and feedforward gist signaling
    Lee, Kwangjun, Dora, Shirin, Mejias, Jorge F., Bohte, Sander M., Pennartz, Cyriel M. A. · 2024 · Frontiers in Computational Neuroscience
36. Encoding of speech in convolutional layers and the brain stem based on language experience
    Gašper Beguš, Alan Zhou, Tian Christina Zhao · 2023 · Scientific Reports
37. A general framework for interpretable neural learning based on local information-theoretic goal functions
    Abdullah Makkeh, Marcel Graetz, Andreas C. Schneider, David A. Ehrlich, Viola Priesemann, Michael Wibral · 2025 · Proceedings of the National Academy of Sciences
38. Self-supervised predictive learning accounts for cortical layer-specificity
    Kevin Kermani Nejad, Paul G. Anastasiades, Loreen Hertäg, Rui Ponte Costa · 2025 · Nature Communications
39. Reflective-net: learning from explanations
    Johannes Schneider, Michail Vlachos · 2023 · Data Mining and Knowledge Discovery
40. Thalamocortical contribution to flexible learning in neural systems
    Mien Brabeeba Wang, Michael M. Halassa · 2022 · Network Neuroscience
41. Rigorous dynamic model of a silicon ring resonator with phase change material for a neuromorphic node
    Alessio Lugnan, Santiago García-Cuevas Carrillo, Christopher David Wright, Peter Bienstman · 2022 · Optics Express
42. Using Project-based Learning in a Hybrid e-Learning System Model
    Luis Alfaro, Claudia Rivera, Jorge Luna-Urquizo · 2019 · International Journal of Advanced Computer Science and Applications
43. Unsigned temporal difference errors in cortical L5 dendrites during learning
    Gwendolin Schoenfeld, Sepp Kollmorgen, Matthias C. Tsai, Chris Lewis, Shuting Han, Philipp Bethge, Anna Maria Reuss, Adriano Aguzzi, Walter Senn, Valerio Mante et al. · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
44. Hebbian learning revisited and its inference underlying cognitive function
    Sukbin Lim · 2021 · Current Opinion in Behavioral Sciences
45. A neural network model of differentiation and integration of competing memories
    Victoria J. H. Ritvo, Alex Nguyen, Nicholas B. Turk‐Browne, Kenneth A. Norman · 2024 · eLife
46. Contrastive Similarity Matching for Supervised Learning
    Shanshan Qin, Nayantara Mudur, Cengiz Pehlevan · 2021 · Neural Computation
47. Meta-Learning Biologically Plausible Semi-Supervised Update Rules
    Keren Gu, Sam Greydanus, Luke Metz, Niru Maheswaranathan, Jascha Sohl‐Dickstein · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
48. Machine learning aided design of Bi2WO6/MIL-53(Al) nanocomposites
    Xiuyun Zhai, Mingtong Chen · 2023 · Computational Materials Science
49. Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma
    Rudroff, Thorsten, Rainio, Oona, Klén, Riku · 2024 · Brain Sciences
50. An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing
    Igor Nunes, Mike Heddes, Tony D. Givargis, Alexandru Eugen Nicolau · 2023 · ACM/IEEE Design Automation Conference (DAC)
51. Monotone Quantifiers Emerge via Iterated Learning
    Fausto Carcassi, Shane Steinert‐Threlkeld, Jakub Szymanik · 2021 · Cognitive Science
52. A neuronal least-action principle for real-time learning in cortical circuits
    Walter Senn, Dominik Dold, Ákos F. Kungl, Benjamin Ellenberger, Jakob Jordan, Yoshua Bengio, João Sacramento, Mihai Alexandru Petrovici · 2023 · eLife
53. Gradient-free training of recurrent neural networks using random perturbations
    Jesús García Fernández, Sander Wessel Keemink, Marcel A. J. van Gerven · 2024 · Frontiers in Neuroscience
54. Reservoir direct feedback alignment: deep learning by physical dynamics
    M. Nakajima, Yongbo Zhang, Katsuma Inoue, Yasuo Kuniyoshi, Toshikazu Hashimoto, Kohei Nakajima · 2024 · Communications Physics
55. Dynamics of specialization in neural modules under resource constraints
    Gabriel Béna, Dan F. M. Goodman · 2025 · Nature Communications
56. Identifying and Localizing Multiple Objects Using Artificial Ventral and Dorsal Cortical Visual Pathways
    Zhixian Han, Anne B. Sereno · 2022 · Neural Computation
57. Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation
    Umais Zahid, Qinghai Guo, Zafeirios Fountas · 2023 · Neural Computation
58. EO-MTRNN: evolutionary optimization of hyperparameters for a neuro-inspired computational model of spatiotemporal learning
    Erhard Wieser, Gordon Cheng · 2020 · Biological Cybernetics
59. Predicting smart cities’ electricity demands using k-means clustering algorithm in smart grid
    Shurui Wang, Aifeng Song, Yufeng Qian · 2023 · Computer Science and Information Systems
60. Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream
    Franziska Geiger, Martin Schrimpf, Tiago Marques, James J. DiCarlo · 2020 · bioRxiv (Cold Spring Harbor Laboratory)
61. Does Deep Learning Have Epileptic Seizures? On the Modeling of the Brain
    Damien Depannemaecker, Léo Pio‐Lopez, Christophe Gauld · 2023 · Cognitive Computation
62. Decoding the application of deep learning in neuroscience: a bibliometric analysis
    Yin Li, Zilong Zhong · 2024 · Frontiers in Computational Neuroscience
63. Active Vision in Binocular Depth Estimation: A Top-Down Perspective
    Matteo Priorelli, Giovanni Pezzulo, Ivilin Peev Stoianov · 2023 · Biomimetics
64. Dynamic planning in hierarchical active inference
    Matteo Priorelli, Ivilin Peev Stoianov · 2025 · Neural Networks
65. Computational mechanisms of distributed value representations and mixed learning strategies
    Shiva Ghaani Farashahi, Alireza Soltani · 2021 · Nature Communications
66. Towards biologically plausible learning in neural networks
    Jesús García Fernández, Enrique Hortal, Siamak Mehrkanoon · 2021 · IEEE Symposium Series on Computational Intelligence (SSCI)
67. Application of hybrid algorithm of bionic heuristic and machine learning in nonlinear sequence
    Lipeng Qian, Changzheng Liu, Jiaxin Yi, Shannan Liu · 2020 · Journal of Physics Conference Series
68. Effects of retrieval practice on tested and untested information: Cortico-hippocampal interactions and error-driven learning
    Xiaonan L. Liu, Randall C. O’Reilly, Charan Ranganath · 2021 · The Psychology of learning and motivation/The psychology of learning and motivation
69. Comparing Sites of Plasticity in Models of Adaptation to Manifold-Based Perturbations in Brain-Computer Interfaces
    Alexandre Payeur, Amy L. Orsborn, Guillaume Lajoie · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
70. Statistical physics of learning in high-dimensional chaotic systems
    Samantha J. Fournier, Pierfrancesco Urbani · 2023 · Journal of Statistical Mechanics Theory and Experiment
71. Neurobiological Causal Models of Language Processing
    Hartmut Fitz, Peter Hagoort A, Karl Magnus Petersson · 2024 · Neurobiology of Language
72. Prediction of residual stresses in welded structures based on neural network: a review
    Yuli Qin, Chunwei Ma, Lin Mei · 2024 · Journal of Materials Science
73. An implemented predictive coding model of lexico-semantic processing explains the dynamics of univariate and multivariate activity within the left ventromedial temporal lobe during reading comprehension
    Lin Wang, Samer Nour Eddine, Trevor A. Brothers, Ole B. Jensen, Gina R. Kuperberg · 2024 · NeuroImage
74. Metallic surface defect detection via NWD-WIoU based on grayscale co-generation entropy gain
    Jinghua Xu, Deliang Ye, Shuyou Zhang, Kang Wang, Shenghao Chen · 2025 · Applied Intelligence
75. Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks
    Mufeng Tang, Yibo Yang, Yali Amit · 2022 · Frontiers in Computational Neuroscience
76. Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network
    Yuna Li, Tongpeng Chu, Yaou Liu, Haicheng Zhang, Fanghui Dong, Qun Gai, Ying–Hong Shi, Heng Ma, Feng Zhao, Kaili Che et al. · 2022 · Journal of Affective Disorders
77. Performance of biologically grounded models of the early visual system on standard object recognition tasks
    Michael Teichmann, René Larisch, Fred Henrik Hamker · 2021 · Neural Networks
78. CSNNs: Unsupervised, Backpropagation-Free Convolutional Neural Networks for Representation Learning
    Bonifaz Stuhr, Jurgen Brauer · 2019 · International Conference on Machine Learning and Applications
79. A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems
    Yunpeng Guo, Xiaolong Zou, Yifan Hu, Yifei Yang, Xinxin Wang, Yuhan He, Ruikai Kong, Yuzheng Guo, Guoqi Li, Wei Zhang et al. · 2021 · Advanced Intelligent Systems
80. A contrastive rule for meta-learning
    Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento · 2021 · Zurich Open Repository and Archive (University of Zurich)
81. Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment
    Alexander G. Ororbia · 2023
82. Parallel development of object recognition in newborn chicks and deep neural networks
    Lalit Pandey, Donsuk Lee, Samantha M. W. Wood, Justin N. Wood · 2024 · PLoS Computational Biology
83. Artificial creativity: can there be creativity without cognition?
    Matteo Da Pelo · 2025 · AI & Society
84. Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features’ Learning Efficacy
    Hyunsu Lee · 2023 · Electronics
85. Deep Learning Scoring Model in the Evaluation of Oral English Teaching
    Yamei Liu, RongQin Li · 2022 · Computational Intelligence and Neuroscience
86. [Retracted] Analysis of the Effect of Classroom Reform of English Literature on the Theme of Environmental Protection in Universities Based on Artificial Intelligence Technology
    Xuefeng Li · 2022 · Journal of Environmental and Public Health · Retracted
87. A neural network model of differentiation and integration of competing memories
    Victoria J. H. Ritvo, Alex Nguyen, Nicholas B. Turk‐Browne, Kenneth A. Norman · 2023 · eLife
88. Hierarchical temporal prediction captures motion processing from retina to higher visual cortex
    Yosef Singer, Ben D. B. Willmore, Andrew J. King, Nicol Spencer Harper · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
89. Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks
    Dina Obeid, Hugo Ramambason, Cengiz Pehlevan · 2019 · arXiv (Cornell University)
90. Cellular and subcellular specialization enables biology-constrained deep learning
    Alessandro R. Galloni, Ajay Peddada, Yash Chennawar, Aaron D. Milstein · 2026 · Cell Reports
91. Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus
    Diogo Santos-Pata, Adrián F. Amil, Ivan Raikov, César Rennó‐Costa, Anna Mura, Iván Soltész, Paul F. M. J. Verschure · 2021 · Trends in Cognitive Sciences
92. Application effect of rehabilitation robots in rehabilitation of limb movement disorders based on neural network algorithms
    Dongchen Han · 2025 · SLAS TECHNOLOGY
93. Design Power Controller for Smart Grid System Based on Internet of Things Devices and Artificial Neural Network
    Muhammad Ridwan Arif Cahyono · 2021 · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)
94. Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning
    Aman Bhargava, Mohammad R. Rezaei, Milad Lankarany · 2022 · AppliedMath
95. Inferring Mechanisms of Auditory Attentional Modulation with Deep Neural Networks
    Ting‐Yu Kuo, Yuanda Liao, Kai Li, Bo Hong, Xiaolin Hu · 2022 · Neural Computation
96. Equilibrium Propagation and (Memristor-based) Oscillatory Neural Networks
    Gianluca Zoppo, Francesco Marrone, Michele Bonnin, Fernando Corinto · 2022 · IEEE International Symposium on Circuits and Systems proceedings
97. Emergent computations in trained artificial neural networks and real brains
    Néstor Parga, Luis Serrano-Fernández, Joan Falcó-Roget · 2023 · Journal of Instrumentation
98. Sensation and Perception
    Iuliia Kotseruba, John K. Tsotsos · 2025 · Oxford University Press eBooks
99. Neural Logic Circuits: An evolutionary neural architecture that can learn and generalize
    Hamit Taner Ünal, Fatih Başçi̇ftçi̇ · 2023 · Knowledge-Based Systems
100. Evolution of neural activity in circuits bridging sensory and abstract knowledge
     Francesca Mastrogiuseppe, Naoki Hiratani, Peter E. Latham · 2023 · eLife