The University of Chicago Header Logo

Connection

Marlene Cohen to Neurons

This is a "connection" page, showing publications Marlene Cohen has written about Neurons.
Connection Strength

3.614
  1. Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations. Nat Neurosci. 2019 10; 22(10):1669-1676.
    View in: PubMed
    Score: 0.381
  2. Stimulus Dependence of Correlated Variability across Cortical Areas. J Neurosci. 2016 07 13; 36(28):7546-56.
    View in: PubMed
    Score: 0.306
  3. Relating normalization to neuronal populations across cortical areas. J Neurophysiol. 2016 09 01; 116(3):1375-86.
    View in: PubMed
    Score: 0.305
  4. Global cognitive factors modulate correlated response variability between V4 neurons. J Neurosci. 2014 Dec 03; 34(49):16408-16.
    View in: PubMed
    Score: 0.274
  5. Pursuing the link between neurons and behavior. Neuron. 2013 Jul 10; 79(1):6-9.
    View in: PubMed
    Score: 0.249
  6. Measuring and interpreting neuronal correlations. Nat Neurosci. 2011 Jun 27; 14(7):811-9.
    View in: PubMed
    Score: 0.216
  7. Using neuronal populations to study the mechanisms underlying spatial and feature attention. Neuron. 2011 Jun 23; 70(6):1192-204.
    View in: PubMed
    Score: 0.216
  8. A neuronal population measure of attention predicts behavioral performance on individual trials. J Neurosci. 2010 Nov 10; 30(45):15241-53.
    View in: PubMed
    Score: 0.207
  9. Estimates of the contribution of single neurons to perception depend on timescale and noise correlation. J Neurosci. 2009 May 20; 29(20):6635-48.
    View in: PubMed
    Score: 0.187
  10. Topological insights into the neural basis of flexible behavior. Proc Natl Acad Sci U S A. 2023 06 13; 120(24):e2219557120.
    View in: PubMed
    Score: 0.123
  11. Dynamic task-belief is an integral part of decision-making. Neuron. 2022 08 03; 110(15):2503-2511.e3.
    View in: PubMed
    Score: 0.115
  12. A general decoding strategy explains the relationship between behavior and correlated variability. Elife. 2022 06 06; 11.
    View in: PubMed
    Score: 0.115
  13. Methylphenidate as a causal test of translational and basic neural coding hypotheses. Proc Natl Acad Sci U S A. 2022 04 26; 119(17):e2120529119.
    View in: PubMed
    Score: 0.114
  14. Attention improves information flow between neuronal populations without changing the communication subspace. Curr Biol. 2021 12 06; 31(23):5299-5313.e4.
    View in: PubMed
    Score: 0.110
  15. Low rank mechanisms underlying flexible visual representations. Proc Natl Acad Sci U S A. 2020 11 24; 117(47):29321-29329.
    View in: PubMed
    Score: 0.104
  16. Circuit Models of Low-Dimensional Shared Variability in Cortical Networks. Neuron. 2019 01 16; 101(2):337-348.e4.
    View in: PubMed
    Score: 0.091
  17. Neuronal population mechanisms of lightness perception. J Neurophysiol. 2018 11 01; 120(5):2296-2310.
    View in: PubMed
    Score: 0.088
  18. A normalization model suggests that attention changes the weighting of inputs between visual areas. Proc Natl Acad Sci U S A. 2017 05 16; 114(20):E4085-E4094.
    View in: PubMed
    Score: 0.081
  19. A Refined Neuronal Population Measure of Visual Attention. PLoS One. 2015; 10(8):e0136570.
    View in: PubMed
    Score: 0.072
  20. Attention can either increase or decrease spike count correlations in visual cortex. Nat Neurosci. 2014 Nov; 17(11):1591-7.
    View in: PubMed
    Score: 0.068
  21. Eppendorf winner. When attention wanders. Science. 2012 Oct 05; 338(6103):58-9.
    View in: PubMed
    Score: 0.059
  22. When attention wanders: how uncontrolled fluctuations in attention affect performance. J Neurosci. 2011 Nov 02; 31(44):15802-6.
    View in: PubMed
    Score: 0.055
  23. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat Neurosci. 2010 Mar; 13(3):369-78.
    View in: PubMed
    Score: 0.049
  24. Coordinated multiplexing of information about separate objects in visual cortex. Elife. 2022 11 29; 11.
    View in: PubMed
    Score: 0.030
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.