ArticlePLoS computational biology2018
Inferring decoding strategies for multiple correlated neural populations.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Dynamical latent state computation in the male macaque posterior parietal cortex.Nature communications · 2023Article
- Sensory Evidence Accumulation Using Optic Flow in a Naturalistic Navigation Task.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Revealing nonlinear neural decoding by analyzing choices.Nature communications · 2021Article
- A cortico-collicular pathway for motor planning in a memory-dependent perceptual decision task.Nature communications · 2021Article
- Stimulus-dependent relationships between behavioral choice and sensory neural responses.eLife · 2021Article
- Low rank mechanisms underlying flexible visual representations.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Tracking the Mind's Eye: Primate Gaze Behavior during Virtual Visuomotor Navigation Reflects Belief Dynamics.Neuron · 2020Article
- Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Visual and Vestibular Selectivity for Self-Motion in Macaque Posterior Parietal Area 7a.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
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Authors and funding
5 authors.
Funding
Abstract
Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.
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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.