ArticleNeural computation2017
An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.
Article in Neural computation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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78 citing papers in PubMed.
- Deep learning without weight symmetry.PNAS nexus · 2026Article
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- 'Backpropagation and the brain' realized in cortical error neuron microcircuits.PLoS computational biology · 2026Article
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Global error signal guides local optimization in mismatch calculation.Nature communications · 2026Article
- Understanding the Brain as a Mentalizing Machine with a Symbol Grounding Function.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Review
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- Beyond the reducing valve: towards a computational neurophenomenology of altered states via deep neural networks.Frontiers in psychology · 2026Article
- The phantastic organ: predictive processing in the brain.Frontiers in psychology · 2026Review
- Backpropagation through space, time and the brain.Nature communications · 2025Article
- Neural Associative Skill Memories for Safer Robotics and Modeling Human Sensorimotor Repertoires.Neural computation · 2025Article
- Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding.PLoS computational biology · 2025Article
- Self-supervised predictive learning accounts for cortical layer-specificity.Nature communications · 2025Article
- Article
- A Multi-Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision-Making.bioRxiv : the preprint server for biology · 2025Article
- Deep Hybrid Models: Infer and Plan in a Dynamic World.Entropy (Basel, Switzerland) · 2025Article
- Brain-like variational inference.ArXiv · 2025Article
- Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Effective methods and framework for energy-based local learning of deep neural networks.Frontiers in artificial intelligence · 2025Article
- Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines.Entropy (Basel, Switzerland) · 2024Article
18 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.
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