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Probabilistic Interpretation of Population Codes
Open accessAbstract
We present a general encoding-decoding framework for interpreting the activity of a population of units. A standard population code interpretation method, the Poisson model, starts from a description as to how a single value of an underlying quantity can generate the activities of each unit in the population. In casting it in the encoding-decoding framework, we find that this model is too restrictive to describe fully the activities of units in population codes in higher processing areas, such as the medial temporal area. Under a more powerful model, the population activity can convey information not only about a single value of some quantity but also about its whole distribution, including its variance, and perhaps even the certainty the system has in the actual presence in the world of the entity generating this quantity. We propose a novel method for forming such probabilistic interpretations of population codes and compare it to the existing method.
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“From this perspective, our neural recordings support the idea of a “Bayesian brain” (66) and a neural mechanism of decision making that does not flip into a fixed point or attractor state but instead approximates the formation of a probability distribution (41, 67).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [67]
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“LIP neurons are hypothesized to encode the attentional salience or expected value of a visual saccade target (52, 53, 63), but these concepts cannot explain the pattern of LIP activity in our experiment.”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [63]
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Metadata
- Authors
- Zemel, R. S., Dayan, P., & Pouget, A.
- Affiliations
- University of Arizona (US)
- Massachusetts Institute of Technology (US)
- Georgetown University (US)
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1998-02-01
- Volume
- 10
- Issue
- 2
- Pages
- 403-430
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W1985162039
- Cited by
- 333
- References
- 32
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 15 · 2023: 9 · 2022: 11 · 2021: 9 · 2020: 11 · 2019: 13 · 2018: 11 · 2017: 16 · 2016: 16 · 2015: 16 · 2014: 15 · 2013: 18 · 2012: 16
- FWCI
- 4.24
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- James S. McDonnell Foundation
- Surdna Foundation
- National Institutes of Health (1r29mh055541-01)
- National Institute of Mental Health (R29 MH 55541-01)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- population coding, probabilistic interpretation, Poisson model, population activity, medial temporal lobe, neural coding, uncertainty representation
- MeSH terms
- Data Interpretation, Statistical, Models, Neurological, Neurons, Probability, Models, Statistical, Poisson Distribution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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