Information-Limiting Correlations in Large Neural Populations Article Swipe
Ramón Bartolo
,
Richard C. Saunders
,
Andrew R. Mitz
,
Bruno B. Averbeck
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1523/jneurosci.2072-19.2019
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1523/jneurosci.2072-19.2019
Understanding the neural code requires understanding how populations of neurons code information. Theoretical models predict that information may be limited by correlated noise in large neural populations. Nevertheless, analyses based on tens of neurons have failed to find evidence of saturation. Moreover, some studies have shown that noise correlations can be very small, and therefore may not affect information coding. To determine whether information-limiting correlations exist, we implanted eight Utah arrays in prefrontal cortex (PFC; area 46) of two male macaque monkeys, recording >500 neurons simultaneously. We estimated information in PFC about saccades as a function of ensemble size. Noise correlations were, on average, small (∼10 −3 ). However, information scaled strongly sublinearly with ensemble size. After shuffling trials, destroying noise correlations, information was a linear function of ensemble size. Thus, we provide evidence for the existence of information-limiting noise correlations in large populations of PFC neurons. SIGNIFICANCE STATEMENT Recent theoretical work has shown that even small correlations can limit information if they are “differential correlations,” which are difficult to measure directly. However, they can be detected through decoding analyses on recordings from a large number of neurons over a large number of trials. We have achieved both by collecting neural activity in dorsal-lateral prefrontal cortex of macaques using eight microelectrode arrays (768 electrodes), from which we were able to compute accurate information estimates. We show, for the first time, strong evidence for information-limiting correlations. Despite pairwise correlations being small (on the order of 10 −3 ), they affect information coding in populations on the order of 100 s of neurons.
Related Topics
Concepts
Neural coding
Macaque
Noise (video)
Pairwise comparison
Computer science
Neural ensemble
Coding (social sciences)
Limiting
Pattern recognition (psychology)
Neuroscience
Statistical physics
Artificial intelligence
Psychology
Statistics
Physics
Mathematics
Image (mathematics)
Mechanical engineering
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1523/jneurosci.2072-19.2019
- https://www.jneurosci.org/content/jneuro/40/8/1668.full.pdf
- OA Status
- bronze
- Cited By
- 98
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2999094619
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2999094619Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1523/jneurosci.2072-19.2019Digital Object Identifier
- Title
-
Information-Limiting Correlations in Large Neural PopulationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-15Full publication date if available
- Authors
-
Ramón Bartolo, Richard C. Saunders, Andrew R. Mitz, Bruno B. AverbeckList of authors in order
- Landing page
-
https://doi.org/10.1523/jneurosci.2072-19.2019Publisher landing page
- PDF URL
-
https://www.jneurosci.org/content/jneuro/40/8/1668.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.jneurosci.org/content/jneuro/40/8/1668.full.pdfDirect OA link when available
- Concepts
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Neural coding, Macaque, Noise (video), Pairwise comparison, Computer science, Neural ensemble, Coding (social sciences), Limiting, Pattern recognition (psychology), Neuroscience, Statistical physics, Artificial intelligence, Psychology, Statistics, Physics, Mathematics, Image (mathematics), Mechanical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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98Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11, 2024: 17, 2023: 24, 2022: 8, 2021: 25Per-year citation counts (last 5 years)
- References (count)
-
77Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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