Marlene Cohen, PhD

Assistant Professor, Neuroscience


115 Mellon Institute
F: 412-268-5060
Website >


PhD, Stanford University (2014)


Using attention to study cortical population codes

Research Summary

My group studies the way visual information is encoded in groups of neurons and used to guide behavior. Thousands of times each day, our brains must evaluate a complex visual scene, extract the important sensory information, and make quick decisions about how to act based on that information. We are interested in neural processes such as visual attention that make it possible to flexibly pick out visual information that is relevant to the task at hand. Our experiments and computational work are also aimed at understanding the principles by which that information is encoded in different stages of the visual pathway.

We use a combination of single and multi-electrode electrophysiology, psychophysics, and computational techniques to study how sensory information is encoded in groups of neurons and the relationship between the activity of different groups of neurons and behavior. The most important part of our approach is to record the responses of many neurons simultaneously. Measuring the responses of groups of neurons gives us a glimpse of the sensory information available to a subject at a given moment and can give insight into which aspects of the population code are important for neural computation and how the responses of visual neurons are related to perceptual decisions.


Cohen, M.R. and Maunsell, J.H.R. When attention wanders: how uncontrolled fluctuations in attention affect performance. Journal of Neuroscience, 31(44): 15802-06, 2011.

Cohen, M.R. and Maunsell, J.H.R. Using neuronal populations to study the mechanisms underlying feature and spatial attention. Neuron, in press.

Cohen, M.R. and Maunsell, J.H.R. A neuronal population measure of attention predicts behavioral performance on individual trials. Journal of Neuroscience, 30:15241-53, 2010.

Cohen, M.R. and Maunsell, J.H.R. Attention improves performance primarily by reducing interneuronal correlations. Nature Neuroscience, 12(12):1594-1600, 2009.

Cohen, M.R. and Newsome, W.T. Estimates of the contribution of single neurons to perception depend on timescale and noise correlation. Journal of Neuroscience, 29:6635-48, 2009.

Cohen, M.R. and Newsome, W.T.. Context-dependent changes in functional circuitry in visual area MT. Neuron, 60(1):162-173.