ArticleFrontiers in artificial intelligence2019
The Generative Adversarial Brain.
Article in Frontiers in artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 92 citations in OpenAlex.
- The hippocampus as a generative model.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Reappraising intuitions about consciousness.Frontiers in psychology · 2026Article
- Beyond the reducing valve: towards a computational neurophenomenology of altered states via deep neural networks.Frontiers in psychology · 2026Article
- Shaping the Space: A Role for the Hippocampus in Mental Imagery Formation.Vision (Basel, Switzerland) · 2025Review
- Sharing the World-A Social Aspect of Consciousness.Open mind : discoveries in cognitive science · 2025Article
- Learning probability distributions of sensory inputs with Monte Carlo predictive coding.PLoS computational biology · 2024Article
- The attentive reconstruction of objects facilitates robust object recognition.PLoS computational biology · 2024Article
- Applying Generative Artificial Intelligence to cognitive models of decision making.Frontiers in psychology · 2024Article
- Introspective inference counteracts perceptual distortion.Nature communications · 2023Article
- Metacognitive awareness in the sound-induced flash illusion.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- A role for cortical interneurons as adversarial discriminators.PLoS computational biology · 2023Article
- Using Generated Object Reconstructions to Study Object-based Attention.Proceedings of the ... Conference on Cognitive Computational Neuroscience · 2023Article
- Individual differences in naturalistic learning link negative emotionality to the development of anxiety.Science advances · 2023Article
- Article
- Theories of consciousness.Nature reviews. Neuroscience · 2022Review
- Article
- Integrated world modeling theory expanded: Implications for the future of consciousness.Frontiers in computational neuroscience · 2022Article
- What if consciousness is not an emergent property of the brain? Observational and empirical challenges to materialistic models.Frontiers in psychology · 2022Review
- From internal models toward metacognitive AI.Biological cybernetics · 2021Article
- The thermodynamics of cognition: A mathematical treatment.Computational and structural biotechnology journal · 2021Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The idea that the brain learns generative models of the world has been widely promulgated. Most approaches have assumed that the brain learns an explicit density model that assigns a probability to each possible state of the world. However, explicit density models are difficult to learn, requiring approximate inference techniques that may find poor solutions. An alternative approach is to learn an implicit density model that can sample from the generative model without evaluating the probabilities of those samples. The implicit model can be trained to fool a discriminator into believing that the samples are real. This is the idea behind generative adversarial algorithms, which have proven adept at learning realistic generative models. This paper develops an adversarial framework for probabilistic computation in the brain. It first considers how generative adversarial algorithms overcome some of the problems that vex prior theories based on explicit density models. It then discusses the psychological and neural evidence for this framework, as well as how the breakdown of the generator and discriminator could lead to delusions observed in some mental disorders.
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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.