ArticleNature communications2022
The neural coding framework for learning generative models.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- pyhgf: A neural network library for predictive coding.PLoS computational biology · 2026Article
- Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding.PLoS computational biology · 2025Article
- Deep Hybrid Models: Infer and Plan in a Dynamic World.Entropy (Basel, Switzerland) · 2025Article
- Learning probability distributions of sensory inputs with Monte Carlo predictive coding.PLoS computational biology · 2024Article
- In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks.Entropy (Basel, Switzerland) · 2024Article
- Predictive coding networks for temporal prediction.PLoS computational biology · 2024Article
- Applying Generative Artificial Intelligence to cognitive models of decision making.Frontiers in psychology · 2024Article
- Deep kinematic inference affords efficient and scalable control of bodily movements.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Recurrent predictive coding models for associative memory employing covariance learning.PLoS computational biology · 2023Article
- Learning on Arbitrary Graph Topologies via Predictive Coding.Advances in neural information processing systems · 2022Article
- Associative Memories via Predictive Coding.Advances in neural information processing systems · 2021Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the theory of predictive processing in the brain. According to predictive processing theory, the neurons in the brain form a hierarchy in which neurons in one level form expectations about sensory inputs from another level. These neurons update their local models based on differences between their expectations and the observed signals. In a similar way, artificial neurons in our generative models predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality. In this work, we show that the neural generative models learned within our framework perform well in practice across several benchmark datasets and metrics and either remain competitive with or significantly outperform other generative models with similar functionality (such as the variational auto-encoder).
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