Marlene Cohen, PhD

Associate Professor, Neuroscience

Contact

115 Mellon Institute
412-268-4486
F: 412-268-5060
cohenm@pitt.edu
Website >

Education

PhD, Stanford University (2007)

Focus

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.

Summer Undergraduate Research Program

Yes

Publications

Ruff DA and Cohen MR (under review). Simultaneous multi-area recordings suggest a novel hypothesis about how attention improves performance. bioRxiv doi: https://doi.org/10.1101/372888

Huang C, Ruff DA, Pyle R, Rosenbaum R, Cohen MR, Doiron B (in press). Circuit-based models of shared variability in cortical networks. Neuron, in press. bioRxiv doi: https://doi.org/10.1101/217976.

Ruff DA*, Ni AM*, and Cohen MR (2018). Cognition as a window into neuronal population space.  Annu Rev Neurosci. 41:77-97. doi: 10.1146/annurev-neuro-080317-061936.

Ruff DA, Brainard DH, and Cohen MR (2018). Neuronal population mechanisms of lightness perception. J Neurophysiology, doi: 10.1152/jn.00906.2017. [Epub ahead of print].

Ni AM, Ruff DA, Alberts JJ, Symmonds J, and Cohen MR (2018). Learning and attention reveal a general relationship between neuronal variability and perception. Science 359(6374), pp. 463-465.

Kanashiro T, Ocker G, Cohen MR, Doiron B (2017). Attentional modulation of neuronal variability in circuit models of cortex, eLife, doi: 10.7554/eLife.23978.

Ruff DA, Cohen MR (2017). A normalization model suggests that attention changes the weighting of inputs between visual areas. Proc Natl Acad Sci. May 2017 pii: 201619857.

Ruff DA, Alberts JJ, and Cohen MR (2016). Relating normalization to neuronal populations across cortical areas.  J Neurophys, 116(3):1375-86.

Ruff DA and Cohen MR (2016).  Attention increases spike count correlations between visual cortical areas. J Neurosci, 36(28): 7453-7463.

Ruff DA and Cohen MR (2016).  Stimulus dependence of correlated variability across cortical areas. J Neurosci, 36(28):7546-7556.

Oby ER, Perel S, Sadtler P, Ruff DA, Mischel JL, Montez DF, Cohen MR, Batista AP, Chase SM (2016). Exracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters. J Neural Eng, 13(3):036009.

Rabinowitz NC, Goris RL, Cohen MR, and Simoncelli EP (2015).  Attention stabilizes the shared gain of V4 populations.  Elife, November 2015 4:e08998.

Mayo JP, Cohen MR, and Maunsell JHR (2015).  A refined neuronal population measure of visual attention. PLOS One, 10(8):e0136570.

Ruff DA and Cohen MR (2014). Global cognitive factors modulate correlated response variability between V4 neurons. Journal of Neuroscience, 34(49):16408-16.

Ruff DA and Cohen MR (2014).  Attention can either increase or decrease spike count correlations in visual cortex, Nature Neuroscience, 17(11):1591-7.

Ruff DA and Cohen MR (2014).  Relating the activity of sensory neurons to perception.  In The Cognitive Neurosciences (MIT Press).

Cohen MR and Maunsell JHR (2014).  Neuronal mechanisms of spatial attention in visual cerebral cortex.  In The Oxford Handbook of Attention. 

Ruff DA and Cohen MR (2013).  Pursuing the link between neurons and behavior. Neuron, 79: 6-9.

Cohen MR (2012).  When attention wanders.  Science 5 October 2012: 338 (6103), 58-59.

Nienborg H*, Cohen MR*, and Cumming BG (2012).  Decision-related activity in sensory neurons:  correlations among neurons and with behavior.  Annu Rev Neurosci, 35: 463-83.

Cohen MR and Kohn AK (2011). Measuring and interpreting neuronal correlations. Nature Neuroscience, 14:811-809.

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

Cohen MR and Maunsell JHR (2011). Using neuronal populations to study the mechanisms underlying spatial and feature attention. Neuron, 70:1192-1204.

Cohen MR and Maunsell JHR (2010). A neuronal population measure of attention predicts behavioral performance on individual trials. Journal of Neuroscience, 30:15241-53.

Churchland MM, Yu BM, Cunningham JP, Sugrue LP, Cohen MR, Corrado GS, Newsome WT, Clark AM Hosseini P, Scott BB, Bradley DC, Smith MA, Kohn A, Movshon JA, Armstrong KM, Moore T, Chang SW, Snyder LH, Priebe NJ, Finn IM, Ferster D, Ryu SI, Santhanam G, Sahani M, and Shenoy KV (2010). Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature Neuroscience, 13(3):369-78.

Cohen MR and Maunsell JHR (2009).  Attention improves performance primarily by reducing interneuronal correlations.  Nature Neuroscience, 12(12):1594-1600.

Cohen MR and Newsome WT (2009). Estimates of the contribution of single neurons to perception depend on timescale and noise correlation, Journal of Neuroscience, 29:6635-48.

Cohen MR and Newsome WT (2008).  Context-dependent changes in functional circuitry in visual area MT.  Neuron, 60(1):162-173.

Barberini CL, Cohen MR, Wandell BA and Newsome WT (2005).   Cone signal interactions in direction-selective neurons in the middle temporal visual area (MT).  Journal of Vision, 5:603-621.

Cohen MR and Newsome WT (2004).  What electrical microstimulation has revealed about the neural basis of cognition.  Current Opinion in Neurobiology, 14:1-9.

Cohen MR, Meissner GW, Schafer RJ, and Raymond JL (2004).  Reversal of motor learning in the vestibulo-ocular reflex in the absence of visual input.  Learning and Memory, 11(5):559-565.