ReviewNeuron2017
Inference in the Brain: Statistics Flowing in Redundant Population Codes.
Review in Neuron, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- Task learning increases information redundancy of neural responses in macaque visual cortex.Science (New York, N.Y.) · 2026Article
- Beyond Divisive Normalization: Scalable Feedforward Networks for Multisensory Integration Across Reference Frames.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Both k-core percolation and directed graph analysis revealed succession and transition of voxels' spatiotemporal progress on dynamic correlation resting-state fMRI.Frontiers in human neuroscience · 2025Article
- Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks.Communications biology · 2024Article
- Decision-related activity and movement selection in primate visual cortex.Science advances · 2024Article
- Response sub-additivity and variability quenching in visual cortex.Nature reviews. Neuroscience · 2024Review
- Population coding of strategic variables during foraging in freely moving macaques.Nature neuroscience · 2024Article
- Accumulation of network redundancy marks the early stage of Alzheimer's disease.Human brain mapping · 2023Article
- Post-injury pain and behaviour: a control theory perspective.Nature reviews. Neuroscience · 2023Review
- Evolutionary dynamics of mushroom body Kenyon cell types in hymenopteran brains from multifunctional type to functionally specialized types.Science advances · 2023Article
- Dynamical latent state computation in the male macaque posterior parietal cortex.Nature communications · 2023Article
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Coding of latent variables in sensory, parietal, and frontal cortices during closed-loop virtual navigation.eLife · 2022Article
- Priority coding in the visual system.Nature reviews. Neuroscience · 2022Review
- Robust Coding of Eye Position in Posterior Parietal Cortex despite Context-Dependent Tuning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Task-induced neural covariability as a signature of approximate Bayesian learning and inference.PLoS computational biology · 2022Article
- Attention improves information flow between neuronal populations without changing the communication subspace.Current biology : CB · 2021Article
- The association between hippocampal volume and memory in pathological aging is mediated by functional redundancy.Neurobiology of aging · 2021Article
- Revealing nonlinear neural decoding by analyzing choices.Nature communications · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors.
Indexed as
Identifiers
What OpenQuestion holds
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.