Literature DB >> 29371470

Learning and attention reveal a general relationship between population activity and behavior.

A M Ni1, D A Ruff1, J J Alberts1, J Symmonds1, M R Cohen2.   

Abstract

Prior studies have demonstrated that correlated variability changes with cognitive processes that improve perceptual performance. We tested whether correlated variability covaries with subjects' performance-whether performance improves quickly with attention or slowly with perceptual learning. We found a single, consistent relationship between correlated variability and behavioral performance, regardless of the time frame of correlated variability change. This correlated variability was oriented along the dimensions in population space used by the animal on a trial-by-trial basis to make decisions. That subjects' choices were predicted by specific dimensions that were aligned with the correlated variability axis clarifies long-standing paradoxes about the relationship between shared variability and behavior.
Copyright © 2018, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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Year:  2018        PMID: 29371470      PMCID: PMC6571104          DOI: 10.1126/science.aao0284

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  19 in total

1.  Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4.

Authors:  Jude F Mitchell; Kristy A Sundberg; John H Reynolds
Journal:  Neuron       Date:  2009-09-24       Impact factor: 17.173

2.  Inferring decoding strategies from choice probabilities in the presence of correlated variability.

Authors:  Ralf M Haefner; Sebastian Gerwinn; Jakob H Macke; Matthias Bethge
Journal:  Nat Neurosci       Date:  2013-01-13       Impact factor: 24.884

Review 3.  Perceptual learning: toward a comprehensive theory.

Authors:  Takeo Watanabe; Yuka Sasaki
Journal:  Annu Rev Psychol       Date:  2014-09-10       Impact factor: 24.137

Review 4.  Measuring and interpreting neuronal correlations.

Authors:  Marlene R Cohen; Adam Kohn
Journal:  Nat Neurosci       Date:  2011-06-27       Impact factor: 24.884

5.  Information-limiting correlations.

Authors:  Rubén Moreno-Bote; Jeffrey Beck; Ingmar Kanitscheider; Xaq Pitkow; Peter Latham; Alexandre Pouget
Journal:  Nat Neurosci       Date:  2014-09-07       Impact factor: 24.884

6.  Perceptual training continuously refines neuronal population codes in primary visual cortex.

Authors:  Yin Yan; Malte J Rasch; Minggui Chen; Xiaoping Xiang; Min Huang; Si Wu; Wu Li
Journal:  Nat Neurosci       Date:  2014-09-07       Impact factor: 24.884

7.  Perceptual learning reduces interneuronal correlations in macaque visual cortex.

Authors:  Yong Gu; Sheng Liu; Christopher R Fetsch; Yun Yang; Sam Fok; Adhira Sunkara; Gregory C DeAngelis; Dora E Angelaki
Journal:  Neuron       Date:  2011-08-25       Impact factor: 17.173

8.  Associative learning enhances population coding by inverting interneuronal correlation patterns.

Authors:  James M Jeanne; Tatyana O Sharpee; Timothy Q Gentner
Journal:  Neuron       Date:  2013-04-24       Impact factor: 17.173

9.  Decision-related activity in sensory neurons reflects more than a neuron's causal effect.

Authors:  Hendrikje Nienborg; Bruce G Cumming
Journal:  Nature       Date:  2009-03-08       Impact factor: 49.962

10.  Attention improves performance primarily by reducing interneuronal correlations.

Authors:  Marlene R Cohen; John H R Maunsell
Journal:  Nat Neurosci       Date:  2009-11-15       Impact factor: 24.884

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  59 in total

1.  Altered functional interactions between neurons in primary visual cortex of macaque monkeys with experimental amblyopia.

Authors:  Katerina Acar; Lynne Kiorpes; J Anthony Movshon; Matthew A Smith
Journal:  J Neurophysiol       Date:  2019-09-25       Impact factor: 2.714

2.  Contribution of Sensory Encoding to Measured Bias.

Authors:  Miaomiao Jin; Lindsey L Glickfeld
Journal:  J Neurosci       Date:  2019-04-23       Impact factor: 6.167

3.  Functional MRI and EEG Index Complementary Attentional Modulations.

Authors:  Sirawaj Itthipuripat; Thomas C Sprague; John T Serences
Journal:  J Neurosci       Date:  2019-05-24       Impact factor: 6.167

4.  Frequency-separated principal component analysis of cortical population activity.

Authors:  Jean-Philippe Thivierge
Journal:  J Neurophysiol       Date:  2020-07-29       Impact factor: 2.714

5.  Learning is shaped by abrupt changes in neural engagement.

Authors:  Aaron P Batista; Steven M Chase; Byron M Yu; Jay A Hennig; Emily R Oby; Matthew D Golub; Lindsay A Bahureksa; Patrick T Sadtler; Kristin M Quick; Stephen I Ryu; Elizabeth C Tyler-Kabara
Journal:  Nat Neurosci       Date:  2021-03-29       Impact factor: 24.884

6.  Low rank mechanisms underlying flexible visual representations.

Authors:  Douglas A Ruff; Cheng Xue; Lily E Kramer; Faisal Baqai; Marlene R Cohen
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-24       Impact factor: 11.205

7.  General learning ability in perceptual learning.

Authors:  Jia Yang; Fang-Fang Yan; Lijun Chen; Jie Xi; Shuhan Fan; Pan Zhang; Zhong-Lin Lu; Chang-Bing Huang
Journal:  Proc Natl Acad Sci U S A       Date:  2020-07-23       Impact factor: 11.205

8.  The Magnitude, But Not the Sign, of MT Single-Trial Spike-Time Correlations Predicts Motion Detection Performance.

Authors:  Alireza Hashemi; Ashkan Golzar; Jackson E T Smith; Erik P Cook
Journal:  J Neurosci       Date:  2018-04-06       Impact factor: 6.167

9.  Local and Global Influences of Visual Spatial Selection and Locomotion in Mouse Primary Visual Cortex.

Authors:  Ethan G McBride; Su-Yee J Lee; Edward M Callaway
Journal:  Curr Biol       Date:  2019-05-02       Impact factor: 10.834

Review 10.  Perceptual Decision-Making: A Field in the Midst of a Transformation.

Authors:  Farzaneh Najafi; Anne K Churchland
Journal:  Neuron       Date:  2018-10-24       Impact factor: 17.173

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