ArticleJournal of computational neuroscience2015
Neural representation of probabilities for Bayesian inference.
Article in Journal of computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 28 citations in OpenAlex.
- A Neurocomputational Model of Observation-Based Decision Making with a Focus on Trust.Brain sciences · 2026Article
- The neural code of perceptual inference.Molecular brain · 2026Review
- The Spatial Coding of Touch Is Defined in Intrinsic, Limb-Specific Coordinates: An EEG Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Task-optimized models of sensory uncertainty reproduce human confidence judgments.bioRxiv : the preprint server for biology · 2025Article
- Neural Responses Underlying Interaural Time Difference Discrimination as a Function of Sensory Reliability in the Barn Owl.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Single trial Bayesian inference by population vector readout in the barn owl's sound localization system.PloS one · 2024Article
- Neural tuning instantiates prior expectations in the human visual system.Nature communications · 2023Article
- Similar network compositions, but distinct neural dynamics underlying belief updating in environments with and without explicit outcomes.NeuroImage · 2022Article
- Investigating the representation of uncertainty in neuronal circuits.PLoS computational biology · 2021Article
- How the known reference weakens the visual oblique effect: a Bayesian account of cognitive improvement by cue influence.Scientific reports · 2020Article
- Article
- Synthesis of Hemispheric ITD Tuning from the Readout of a Neural Map: Commonalities of Proposed Coding Schemes in Birds and Mammals.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Emergence of an Adaptive Command for Orienting Behavior in Premotor Brainstem Neurons of Barn Owls.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Development of a Bayesian Estimator for Audio-Visual Integration: A Neurocomputational Study.Frontiers in computational neuroscience · 2017Article
- Cue Reliability Represented in the Shape of Tuning Curves in the Owl's Sound Localization System.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Bayesian models are often successful in describing perception and behavior, but the neural representation of probabilities remains in question. There are several distinct proposals for the neural representation of probabilities, but they have not been directly compared in an example system. Here we consider three models: a non-uniform population code where the stimulus-driven activity and distribution of preferred stimuli in the population represent a likelihood function and a prior, respectively; the sampling hypothesis which proposes that the stimulus-driven activity over time represents a posterior probability and that the spontaneous activity represents a prior; and the class of models which propose that a population of neurons represents a posterior probability in a distributed code. It has been shown that the non-uniform population code model matches the representation of auditory space generated in the owl's external nucleus of the inferior colliculus (ICx). However, the alternative models have not been tested, nor have the three models been directly compared in any system. Here we tested the three models in the owl's ICx. We found that spontaneous firing rate and the average stimulus-driven response of these neurons were not consistent with predictions of the sampling hypothesis. We also found that neural activity in ICx under varying levels of sensory noise did not reflect a posterior probability. On the other hand, the responses of ICx neurons were consistent with the non-uniform population code model. We further show that Bayesian inference can be implemented in the non-uniform population code model using one spike per neuron when the population is large and is thus able to support the rapid inference that is necessary for sound localization.
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.