ArticleNature machine intelligence2020
A neural network trained for prediction mimics diverse features of biological neurons and perception.
Article in Nature machine intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Predictive processing as a scalable computational principle for embodied multitask intelligence.Science advances · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Perceiving the uncertain: predictive coding and individual differences in the dynamics of multistable perception.Cognitive processing · 2026Review
- Predictive networks generate motion-induced color illusions.Scientific reports · 2026Article
- Hierarchical recurrent temporal prediction as a model of the mammalian dorsal visual pathway.PLoS computational biology · 2026Article
- Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks.Nature communications · 2026Article
- Perceptual distortions in PredNet and quantification of top-down/bottom-up flow.Frontiers in computational neuroscience · 2026Article
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- Article
- Multimodal evidence challenges the effectiveness of probabilistic cueing for establishing sensory expectations.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Adaptation optimizes sensory encoding for future stimuli.PLoS computational biology · 2025Article
- Poisson Variational Autoencoder.ArXiv · 2024Article
- Modeling human activity comprehension at human scale: prediction, segmentation, and categorization.PNAS nexus · 2024Article
- Brain-like border ownership signals support prediction of natural videos.bioRxiv : the preprint server for biology · 2024Article
- Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex.PLoS computational biology · 2024Article
- Predictive coding with spiking neurons and feedforward gist signaling.Frontiers in computational neuroscience · 2024Article
- Reconstructing visual illusory experiences from human brain activity.Science advances · 2023Article
- Article
- Artificial-Neural-Network-Driven Innovations in Time-Varying Process Diagnosis of Low-K Oxide Deposition.Sensors (Basel, Switzerland) · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent work has shown that convolutional neural networks (CNNs) trained on image recognition tasks can serve as valuable models for predicting neural responses in primate visual cortex. However, these models typically require biologically-infeasible levels of labeled training data, so this similarity must at least arise via different paths. In addition, most popular CNNs are solely feedforward, lacking a notion of time and recurrence, whereas neurons in visual cortex produce complex time-varying responses, even to static inputs. Towards addressing these inconsistencies with biology, here we study the emergent properties of a recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the resulting model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single-unit response dynamics to complex perceptual motion illusions, even when subjected to highly impoverished stimuli. These results suggest potentially deep connections between recurrent predictive neural network models and computations in the brain, providing new leads that can enrich both fields.
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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.