ReviewNature reviews. Neuroscience2020
Backpropagation and the brain.
Review in Nature reviews. Neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 231 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
231 citing papers in PubMed, 897 citations in OpenAlex.
- Instructive signals drive learning through behavioral timescale synaptic plasticity in the hippocampus and beyond.Current research in neurobiology · 2026Article
- The neuroscience of transformers.Cognitive neurodynamics · 2026Article
- Diffusion of neuromodulators for temporal credit assignment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Representational learning by optimization of neural manifolds in an olfactory memory network.Nature neuroscience · 2026Article
- De novo motor learning as controller synthesis: a conceptual framework.Biological cybernetics · 2026Article
- Synaptic-state-dependent plasticity routes learning signals in recurrent circuits.bioRxiv : the preprint server for biology · 2026Article
- Adaptive Residual Correction Network for Efficient Single Image Super-Resolution.Sensors (Basel, Switzerland) · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Cerebellum-inspired neural network of supervised learning with tensor-based sparse coding for multi-class classification.PLoS computational biology · 2026Article
- A Synchronization-Driven Learning Rule for Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.Research square · 2026Article
- Task-State fMRI-Derived Whole-Brain Functional Topology-Constrained Spiking Neural Network with an Embedded Auditory Core Circuit for Speech Recognition.Biomimetics (Basel, Switzerland) · 2026Article
- Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training.Nature communications · 2026Article
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- The curriculum effect in visual learning: The role of readout dimensionality.PLoS computational biology · 2026Article
- Critical period plasticity enables credit assignment.bioRxiv : the preprint server for biology · 2026Article
- System-level FPGA validation of a trainable and robust multiplier-free spiking neural network.iScience · 2026Article
- Synaptic Plasticity as a Function of the Temporal Derivative.bioRxiv : the preprint server for biology · 2026Article
- Modeling attention and binding in the brain through bidirectional recurrent gating.Nature communications · 2026Article
- Article
- BrainTwin-AI: A Multimodal MRI-EEG-Based Cognitive Digital Twin for Real-Time Brain Health Intelligence.Brain sciences · 2026Article
171 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in deep artificial neural networks, but historically it has been viewed as biologically problematic. Nonetheless, recent developments in neuroscience and the successes of artificial neural networks have reinvigorated interest in whether backpropagation offers insights for understanding learning in the cortex. The backpropagation algorithm learns quickly by computing synaptic updates using feedback connections to deliver error signals. Although feedback connections are ubiquitous in the cortex, it is difficult to see how they could deliver the error signals required by strict formulations of backpropagation. Here we build on past and recent developments to argue that feedback connections may instead induce neural activities whose differences can be used to locally approximate these signals and hence drive effective learning in deep networks in the brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.