Neuroscience at The University of Chicago

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Ph.D., Massachusetts Institute of Technology




Lab Website

David Freedman, Ph.D.

Associate Professor

Research Interests
  • Computational & Theoretical
  • Systems / Behavior / Cognitive
We depend on our ability to see in order to make sense of the world around us and to guide our decisions. Whether navigating a city street or searching for your car keys, you depend on your visual system to successfully interact with and learn about the world around you. A central goal of our research is to understand how the brain uses visual information to guide decision making and learning. A mechanistic understanding of these essential cognitive functions is needed to develop the next generation of treatments for neurological diseases, brain injury and mental illness.

We use sophisticated neurophysiological techniques to monitor the activity of neuronal ensembles during behavioral tasks which require visual recognition, decision making, and learning. To identify key computational mechanisms used by the brain, we employ advanced quantitative and statistical approaches, often in collaboration with our colleagues in mathematics and computer science. Examples of several areas of particular interest to our laboratory are described below.

Categorization – How does the brain recognize the category, or meaning, of incoming visual stimuli? To examine this, we compare neuronal encoding across a brain-wide network of visual and cognitive areas during performance of visual categorization tasks.

Decision Making – How does the brain use visual inputs to flexibly guide our decisions? By designing novel behavioral paradigms in concert with neuronal population recordings, we are directly examining how visual representations are transformed into task-appropriate decisions and actions.

Learning and Memory – How does learning impact neuronal representations, and lead to the storage of new memories? We monitor neuronal ensembles in real time during learning to gain a deeper understanding about how learning shapes neuronal representations and forms lasting memories.

We are always looking for talented and enthusiastic new lab members, including postdoctoral fellows, graduate students, research technicians and undergraduates. Interested candidates are encouraged to contact us directly. Prospective graduate students are encouraged to apply to the Ph.D. programs in Neurobiology or Computational Neuroscience here at The University of Chicago.

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Select Publications

McKee J.L., Riesenhuber M., Miller E.K., and Freedman D.J. Task Dependence of Visual and Category Representations in Prefrontal and Inferior Temporal Cortices. Journal of Neuroscience, in press.

Murray J.D., Bernacchia A., Freedman D.J., Romo R., Wallis J.D., Cai X., Padoa-Schioppa C., Pasternak T., Seo, H., Lee D., and Wang X.J. A Hierarchy of Intrinsic Timescales Across Primate Cortex. Nature Neuroscience, in press.

Ibos G. and Freedman D.J. Dynamic integration of task-relevant visual features in posterior parietal cortex. Neuron, 83: 1-13, 2014.

Swaminathan S.K.*, Masse N.Y.*, and Freedman D.J. A Comparison of Medial and Lateral Intraparietal Areas During a Visual Categorization Task. Jounral of Neuroscience, 33: 13157-13170, 2013. (* equal contributions)

Rishel C.A., Huang G., and Freedman D.J. Independent Category and Spatial Encoding in Parietal Cortex. Neuron, 77: 969-979, 2013.

Fitzgerald J.K., Freedman D.J., Fanini A., Bennur S., Gold J.I., and Assad J.A. Biased Associative Representations in Parietal Cortex. Neuron, 77: 180-191, 2013.

Swaminathan S.K. and Freedman D.J. Preferential Encoding of Visual Categories in Parietal Cortex Compared with Prefrontal Cortex. Nature Neuroscience, 15: 315-320, 2012.

Fitzgerald J.K, Freedman D.J., and Assad J.A. Generalized Associative Representations in Parietal Cortex. Nature Neuroscience, 14: 1075-1079, 2011.

Freedman D.J. and Assad J.A. A Proposed Common Neural Mechanisms for Categorization and Perceptual Decisions. Nature Neuroscience, 14:143-146, 2011.

Freedman D.J. and Assad J.A. Distinct Encoding of Spatial and Non-Spatial Factors in Parietal Cortex. Journal of Neuroscience, 29: 5671-5680, 2009.

Meyers E.M., Freedman D.J., Krieman G., Poggio T., and Miller E.K. Using Neuron Population Readout to Decode the Temporal Dynamics of Category Information. Journal of Neurophysiology 100: 1407-1419, 2008.

Freedman D.J. Neuronal Mechanisms of Visual Categorization and Category Learning. In: The Neuroscience of Rule-Guided Behavior. Wallis J.D. and Bunge S. (eds.). Oxford University Press, pp 391-418, 2007.

Freedman D.J. and Assad J.A. Experience-Dependent Representation of Visual Categories in Parietal Cortex. Nature 443: 85-88, 2006.

Freedman D.J., Riesenhuber M., Poggio T., and Miller E.K. Experience-Dependent Sharpening of Visual Shape Selectivity in Inferior Temporal Cortex. Cerebral Cortex, 16: 1631-1644, 2006.

Miller E.K., Nieder A., Freedman D.J., and Wallis J.D. Neural Correlates of Categories and Concepts. Current Opinion in Neurobiology, 13:2:198-203, 2003.

Freedman D.J., Riesenhuber M., Poggio T., and Miller E.K. A Comparison of Primate Prefrontal and Inferior Temporal Cortices During Visual Categorization. Journal of Neuroscience 23: 5235-5246, 2003.

Nieder A., Freedman D.J., and Miller E.K. Representation of the Quantity of Visual Items in the Primate Prefrontal Cortex. Science 297: 1708-1711, 2002.

Freedman D.J., Riesenhuber M., Poggio T., and Miller E.K. Visual Categorization and the Primate Prefrontal Cortex: Neurophysiology and Behavior. Journal of Neurophysiology 88: 914-928, 2002.

Freedman D.J., Riesenhuber M., Poggio T., Miller E.K. Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex. Science 291: 312-316, 2001.