ArticlePLoS computational biology2015
Optimal Prediction of Moving Sound Source Direction in the Owl.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- 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
- Dynamic spectral cues do not affect human sound localization during small head movements.Frontiers in neuroscience · 2023Article
- Optimal nonlinear cue integration for sound localization.Journal of computational neuroscience · 2017Article
- An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity.Frontiers in neurorobotics · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Capturing nature's statistical structure in behavioral responses is at the core of the ability to function adaptively in the environment. Bayesian statistical inference describes how sensory and prior information can be combined optimally to guide behavior. An outstanding open question of how neural coding supports Bayesian inference includes how sensory cues are optimally integrated over time. Here we address what neural response properties allow a neural system to perform Bayesian prediction, i.e., predicting where a source will be in the near future given sensory information and prior assumptions. The work here shows that the population vector decoder will perform Bayesian prediction when the receptive fields of the neurons encode the target dynamics with shifting receptive fields. We test the model using the system that underlies sound localization in barn owls. Neurons in the owl's midbrain show shifting receptive fields for moving sources that are consistent with the predictions of the model. We predict that neural populations can be specialized to represent the statistics of dynamic stimuli to allow for a vector read-out of Bayes-optimal predictions.
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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.