Marlene Cohen
American neuroscientist
Marlene Cohen's AcademicInfluence.com Rankings

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Biology
Marlene Cohen's Degrees
- PhD Neuroscience University of California, San Francisco
- Bachelors Neurobiology University of California, Berkeley
Why Is Marlene Cohen Influential?
(Suggest an Edit or Addition)According to Wikipedia, Marlene R. Cohen is a neuroscientist at the University of Pittsburgh and an Associate Director of the Center for the Neural Basis of Cognition, a joint venture between the University of Pittsburgh and Carnegie Mellon University. Her team investigates how visual information is encoded in groups of neurons and used to guide behavior. She is recognized for pioneering use of multielectrode array recording to determine that the improved behavioral performance associated with redirecting spatial attention has a neural correlate in the brain that is reflected by reduced correlated activity between neurons. Cohen has also demonstrated that this same mechanism happens during learning. She has received several awards for her work, including the Troland Research Award from the National Academy of Sciences in 2018
Marlene Cohen's Published Works
Published Works
- Attention improves performance primarily by reducing interneuronal correlations (2009) (986)
- Stimulus onset quenches neural variability: a widespread cortical phenomenon (2010) (939)
- Measuring and interpreting neuronal correlations (2011) (910)
- Context-Dependent Changes in Functional Circuitry in Visual Area MT (2008) (253)
- Estimates of the Contribution of Single Neurons to Perception Depend on Timescale and Noise Correlation (2009) (225)
- Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention (2011) (199)
- Decision-related activity in sensory neurons: correlations among neurons and with behavior. (2012) (196)
- What electrical microstimulation has revealed about the neural basis of cognition (2004) (173)
- A Neuronal Population Measure of Attention Predicts Behavioral Performance on Individual Trials (2010) (167)
- Attention can either increase or decrease spike count correlations in visual cortex (2014) (157)
- Attention stabilizes the shared gain of V4 populations (2015) (155)
- Learning and attention reveal a general relationship between population activity and behavior (2018) (129)
- Circuit Models of Low-Dimensional Shared Variability in Cortical Networks (2019) (110)
- Attention Increases Spike Count Correlations between Visual Cortical Areas (2016) (73)
- Cone signal interactions in direction-selective neurons in the middle temporal visual area (MT). (2005) (65)
- Attentional modulation of neuronal variability in circuit models of cortex (2017) (64)
- When Attention Wanders: How Uncontrolled Fluctuations in Attention Affect Performance (2011) (55)
- Stimulus Dependence of Correlated Variability across Cortical Areas (2016) (53)
- Global Cognitive Factors Modulate Correlated Response Variability between V4 Neurons (2014) (42)
- Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations (2018) (37)
- Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters (2016) (29)
- Relating normalization to neuronal populations across cortical areas. (2016) (28)
- Cognition as a Window into Neuronal Population Space. (2018) (28)
- A normalization model suggests that attention changes the weighting of inputs between visual areas (2017) (26)
- Reversal of motor learning in the vestibulo-ocular reflex in the absence of visual input. (2004) (20)
- Attention can increase or decrease spike count correlations between pairs of neurons depending on their role in a task (2014) (20)
- Attention stabilizes the shared gain of V4 (2015) (17)
- Low rank mechanisms underlying flexible visual representations (2019) (13)
- A Refined Neuronal Population Measure of Visual Attention (2015) (13)
- Simultaneous multi-area recordings suggest a novel hypothesis about how attention improves performance (2018) (11)
- Priority coding in the visual system (2022) (11)
- Attention improves information flow between neuronal populations without changing the communication subspace (2021) (10)
- Learning and attention reveal a general relationship between neuronal variability and perception (2017) (10)
- Neuronal population mechanisms of lightness perception (2018) (8)
- Neuronal Mechanisms of Spatial Attention in Visual Cerebral Cortex (2014) (7)
- Circuit-based models of shared variability in cortical networks (2017) (6)
- Author response: Attention stabilizes the shared gain of V4 populations (2015) (5)
- When Attention Wanders (2012) (5)
- A general decoding strategy explains the relationship between behavior and correlated variability (2020) (4)
- Pursuing the Link between Neurons and Behavior (2013) (3)
- Targeted comodulation supports flexible and accurate decoding in V1 (2021) (3)
- Dynamic task-belief is an integral part of decision-making (2021) (2)
- Topological insights into the neural basis of flexible behavior (2021) (2)
- Methylphenidate as a causal test of translational and basic neural coding hypotheses (2021) (2)
- Coordinated multiplexing of information about separate objects in visual cortex (2022) (1)
- Author response: Attentional modulation of neuronal variability in circuit models of cortex (2017) (1)
- Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex (2016) (1)
- Multi-neuron approaches to studying visual perception and decision-making (2021) (0)
- Targeted V1 comodulation supports task-adaptive sensory decisions (2023) (0)
- Neuronal population decoding can account for perceptual lightness illusions (2014) (0)
- Absence of Visual Input Reversal of Motor Learning in the Vestibulo-Ocular Reflex in the References (2004) (0)
- Dynamical activity patterns in the macaque posterior parietal cortex during path integration (2017) (0)
- Overcoming coarse coding in visual cortex via multiplexing: neural correlations differ dramatically when stimulus bundles are presented (2019) (0)
- Patterns of neural correlations in V1 vary with the number of objects (2019) (0)
- Priority coding in the visual system (2022) (0)
- Contributions of the early visual system to high-level visual distinctions (2021) (0)
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What Schools Are Affiliated With Marlene Cohen?
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