Marlene R. Cohen

发表

Learning and attention reveal a general relationship between neuronal variability and perception

Amy M. Ni, Douglas A. Ruff, D. Ruff, 2017, bioRxiv.

When Attention Wanders

Marlene R. Cohen, 2012, Science.

A Refined Neuronal Population Measure of Visual Attention

John H. R. Maunsell, J. Patrick Mayo, J. Maunsell, 2015, PloS one.

Simultaneous multi-area recordings suggest a novel hypothesis about how attention improves performance

Douglas A. Ruff, M. Cohen, D. Ruff, 2018, bioRxiv.

Low rank mechanisms underlying flexible visual representations

M. Cohen, D. Ruff, Cheng Xue, 2019, Proceedings of the National Academy of Sciences.

When Attention Wanders: How Uncontrolled Fluctuations in Attention Affect Performance

J. Maunsell, M. Cohen, Marlene R. Cohen, 2011, The Journal of Neuroscience.

Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention

J. Maunsell, M. Cohen, Marlene R. Cohen, 2011, Neuron.

Context-Dependent Changes in Functional Circuitry in Visual Area MT

W. Newsome, M. Cohen, Marlene R. Cohen, 2008, Neuron.

Multiple objects evoke fluctuating responses in several regions of the visual pathway

Akinori F. Ebihara, Na Young Jun, W. Freiwald, 2023, bioRxiv.

A Neuronal Population Measure of Attention Predicts Behavioral Performance on Individual Trials

J. Maunsell, M. Cohen, Marlene R. Cohen, 2010, The Journal of Neuroscience.

Stimulus onset quenches neural variability: a widespread cortical phenomenon

Andrew M. Clark, Byron M. Yu, Nicholas J. Priebe, 2010, Nature Neuroscience.

Estimates of the Contribution of Single Neurons to Perception Depend on Timescale and Noise Correlation

W. Newsome, M. Cohen, Marlene R. Cohen, 2009, The Journal of Neuroscience.

Attention improves information flow between neuronal populations without changing the communication subspace

M. Cohen, D. Ruff, Marlene R. Cohen, 2021, Current Biology.

Circuit Models of Low-Dimensional Shared Variability in Cortical Networks

Brent Doiron, Robert Rosenbaum, Douglas A. Ruff, 2019, Neuron.

Attentional modulation of neuronal variability in circuit models of cortex

Brent Doiron, Marlene R Cohen, Gabriel Koch Ocker, 2017, eLife.

Measuring and interpreting neuronal correlations

M. Cohen, A. Kohn, Marlene R. Cohen, 2011, Nature Neuroscience.

Attention improves performance primarily by reducing interneuronal correlations

J. Maunsell, M. Cohen, Marlene R. Cohen, 2009, Nature Neuroscience.

Priority coding in the visual system

Nicole C. Rust, M. Cohen, Marlene R. Cohen, 2022, Nature Reviews Neuroscience.

Priority coding in the visual system

Nicole C. Rust, M. Cohen, Marlene R. Cohen, 2022, Nature Reviews Neuroscience.

Cognition as a Window into Neuronal Population Space.

Amy M. Ni, Douglas A Ruff, Marlene R Cohen, 2018, Annual review of neuroscience.

Attention stabilizes the shared gain of V4 populations

Eero P. Simoncelli, Robbe L. T. Goris, Neil C. Rabinowitz, 2015, eLife.

Attention Increases Spike Count Correlations between Visual Cortical Areas

Douglas A Ruff, M. Cohen, D. Ruff, 2016, The Journal of Neuroscience.

Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations

Douglas A Ruff, M. Cohen, D. Ruff, 2019, Nature Neuroscience.

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

Amy M. Ni, D. A. Ruff, M. Cohen, 2018, Science.

Bruce G Cumming, Hendrikje Nienborg, M. Cohen, 2012, Annual review of neuroscience.

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters

Sagi Perel, Steven M Chase, Emily R Oby, 2016, Journal of neural engineering.

What electrical microstimulation has revealed about the neural basis of cognition

W. Newsome, M. Cohen, Marlene R. Cohen, 2004, Current Opinion in Neurobiology.

Simultaneous multi-area recordings suggest a novel hypothesis about how attention improves performance

D. Ruff, Marlene R. Cohen, 2018, bioRxiv.

Stimulus Dependence of Correlated Variability across Cortical Areas

Douglas A Ruff, M. Cohen, D. Ruff, 2016, The Journal of Neuroscience.

Coordinated multiplexing of information about separate objects in visual cortex

Na Young Jun, M. Cohen, D. Ruff, 2022, bioRxiv.

Overcoming coarse coding in visual cortex via multiplexing: neural correlations differ dramatically when stimulus bundles are presented

Na Young Jun, M. Cohen, D. Ruff, 2019 .

Dynamic task-belief is an integral part of decision-making

M. Cohen, Cheng Xue, Lily E. Kramer, 2021, Neuron.

Targeted V1 comodulation supports task-adaptive sensory decisions

Eero P. Simoncelli, D. Ruff, Cristina Savin, 2023, bioRxiv.

A general decoding strategy explains the relationship between behavior and correlated variability

Amy M. Ni, M. Cohen, B. Doiron, 2020, bioRxiv.

Global Cognitive Factors Modulate Correlated Response Variability between V4 Neurons

Douglas A Ruff, M. Cohen, D. Ruff, 2014, The Journal of Neuroscience.

Attention can increase or decrease spike count correlations between pairs of neurons depending on their role in a task

Douglas A Ruff, M. Cohen, D. Ruff, 2014, Nature Neuroscience.

Neuronal population mechanisms of lightness perception

M. Cohen, D. Brainard, D. Ruff, 2018, bioRxiv.

Patterns of neural correlations in V1 vary with the number of objects

Na Young Jun, M. Cohen, D. Ruff, 2019 .

Relating normalization to neuronal populations across cortical areas.

M. Cohen, D. Ruff, J. J. Alberts, 2016, Journal of neurophysiology.

Cone signal interactions in direction-selective neurons in the middle temporal visual area (MT).

Brian A Wandell, William T Newsome, W. Newsome, 2005, Journal of vision.

A normalization model suggests that attention changes the weighting of inputs between visual areas

Douglas A Ruff, M. Cohen, D. Ruff, 2017, Proceedings of the National Academy of Sciences.

Reversal of motor learning in the vestibulo-ocular reflex in the absence of visual input.

Jennifer L Raymond, Robert J Schafer, M. Cohen, 2004, Learning & memory.

Absence of Visual Input Reversal of Motor Learning in the Vestibulo-Ocular Reflex in the References

M. Cohen, G. Meissner, R. J. Schafer, 2004 .

Circuit Models of Low-Dimensional Shared Variability in Cortical Networks

B. Doiron, D. Ruff, Chengcheng Huang, 2019, Neuron.

Circuit-based models of shared variability in cortical networks

B. Doiron, D. Ruff, Chengcheng Huang, 2017, bioRxiv.