A. David Redish, PhD

Professor, Department of Neuroscience

A. David Redish

Contact Info

redish@umn.edu

Office Phone 612-626-3738

Office Address:
4-142 MCB

Lab Address:
4-162 MCB

Professor, Department of Neuroscience

Distinguished McKnight University Professor


Summary

David Redish is a Distinguished McKnight University Professor in the Department of Neuroscience. He and his team explore the computational processes that underlie decision-making. His research addresses questions of addiction from the perspective of addiction as dysfunctions in those decision-making processes. His research interests span the neurophysiology of behavior, including computational, experimental, theoretical, and clinical approaches. His laboratory has major research efforts in theoretical explanations of the interactions of multiple decision-making systems, in the neurophysiology of the information processing in those decision-making systems, and in the clinical consequences of dysfunction in those decision-making systems. Through collaborations with other neuroscientists and psychologists translating their novel decision tasks to human populations, and clinicians testing consequences of their proposed explanations for dysfunction, Dr. Redish and his team explore the similarities and differences across species as a means of understanding addiction and its treatment.

Research

Research Summary/Interests

Behavior, decision-making and information processing in neural systems

My lab has two main research objectives. The first is to further our understanding of how multiple learning and memory systems interact to produce behavior. The second is to apply the theories that arise from the neurophysiology and computational modeling to explain dysfunctional and broken behavioral-control systems, as occurs in addiction. To meet these objectives, the lab combines multi-electrode neural ensemble recordings from awake, behaving animals with complex computational analysis techniques that enable measurement of neural dynamics at very fast time scales (e.g. msec). The lab also builds computational models at all scales (single-neuron compartmental models to large-scale systemic models to abstract algorithmic models) to connect the multiple levels of neurophysiology and behavior. Modern neuroscience sees the brain as an information-processing device.

Understanding how the brain processes information requires understanding the representations used by the network of neurons that compose the brain. However, representations in the brain are distributed: each cell carries only a small portion of the total information. I am interested in questions of how neural structures work together to create systems able to accomplish behavioral tasks.

More specifically, we have ongoing projects in

  • the dynamics of neural ensemble activity in multiple systems (hippocampus, dorsal, ventral striatum, orbitofrontal cortex) during learning,
  • the interaction between multiple learning systems (such as hippocampus and striatum) in the ability to accomplish complex tasks,
  • computational models of addiction and other disorders.

Read more about research objectives and projects on the Redish Lab site

Publications

Ferrante, M, Redish, D, Oquendo, MA, Averbeck, BB, Kinnane, ME & Gordon, JA 2019, 'Computational psychiatry: a report from the 2017 NIMH workshop on opportunities and challenges' Molecular psychiatry, vol. 24, no. 4, pp. 479-483. https://doi.org/10.1038/s41380-018-0063-z

Redish, D, Kazinka, R & Herman, AB 2019, 'Taking an engineer's view: Implications of network analysis for computational psychiatry' Behavioral and Brain Sciences, vol. 42, e24. https://doi.org/10.1017/S0140525X18001152

Walters, CJ, Jubran, J, Sheehan, A, Erickson, MT & Redish, D 2019, 'Avoid-approach conflict behaviors differentially affected by anxiolytics: implications for a computational model of risky decision-making' Psychopharmacology. https://doi.org/10.1007/s00213-019-05197-0

Sweis, BM, Redish, D & Thomas, MJ 2018, 'Prolonged abstinence from cocaine or morphine disrupts separable valuations during decision conflict' Nature Communications, vol. 9, no. 1, 2521. https://doi.org/10.1038/s41467-018-04967-2

Sweis, BM, Thomas, MJ & Redish, D 2018, 'Beyond simple tests of value: Measuring addiction as a heterogeneous disease of computation-specific valuation processes' Learning and Memory, vol. 25, no. 9, pp. 501-512. https://doi.org/10.1101/lm.047795.118

Hasz, BM & Redish, D 2018, 'Deliberation and procedural automation on a two-step task for rats' Frontiers in Integrative Neuroscience, vol. 12, 30. https://doi.org/10.3389/fnint.2018.00030

Sweis, BM, Abram, SV, Schmidt, BJ, Seeland, KD, MacDonald, A, Thomas, MJ & Redish, D 2018, 'Sensitivity to “sunk costs” in mice, rats, and humans' Science, vol. 361, no. 6398, pp. 178-181. https://doi.org/10.1126/science.aar8644

Sweis, BM, Larson, EB, Redish, D & Thomas, MJ 2018, 'Altering gain of the infralimbic-to-accumbens shell circuit alters economically dissociable decision-making algorithms' Proceedings of the National Academy of Sciences of the United States of America, vol. 115, no. 27, pp. E6347-E6355. https://doi.org/10.1073/pnas.1803084115

Sweis, BM, Thomas, MJ & Redish, D 2018, 'Mice learn to avoid regret' PLoS Biology, vol. 16, no. 6, e2005853. https://doi.org/10.1371/journal.pbio.2005853

Redish, D, Kummerfeld, E, Morris, RL & Love, AC 2018, 'Reproducibility failures are essential to scientific inquiry' Proceedings of the National Academy of Sciences of the United States of America, vol. 115, no. 20, pp. 5042-5046. https://doi.org/10.1073/pnas.1806370115

Teaching

Courses

NSCI 3100: Mind and Brain (Spring semester)

Media

In The News

Science

Wired

BBC