Alireza Soltani
Associate Professor
Appointments
Associate Professor and Director of Graduate Studies
Area of Expertise
Computational and Cognitive Neuroscience
Biography
My research mainly focuses on understanding adaptive decision making and learning. Specifically, I am interested in exploring neural mechanisms adaptive processes and how computations required for flexibility in behavior are performed by neuronal elements in the brain. I use detailed computational modeling at different levels (synaptic, cellular, and network), as well as psychophysics and behavioral studies in humans, to look for feasible mechanisms that account for both behavioral and neural data. The ultimate goal of my research is to bridge the gap between cognitive and neuronal processes, and further explain behavioral laws in terms of biophysical parameters and constraints.
Education
B.Sc., Sharif University of Technology
M.Sc., Sharif University of Technology
Ph.D., Brandeis University
Postdoc, California Institute of Technology
Postdoc, Baylor College of Medicine
Taught Courses
Publications
Trepka E, Spitmaan MM, Bari BA, Costa V, Cohen JY, Soltani A (2021). Entropy-Based Metrics for Predicting Choice Behavior Based on Local Response to Reward. Nature Communications, 12:6567.
Spitmaan MM, Seo H, Lee D, Soltani A (2020). Multiple Timescales of Neural Dynamics and Integration of Task-relevant Signals across Cortex. Proceedings of the National Academy of Sciences, 117 (36), 22522-22531
Farashahi S, Donahue C, Hayden B, Lee D, Soltani A (2019). Flexible Combination of Reward Information across Primates. Nature Human Behaviour, 3(11), 1215-1224
Soltani A, Izquierdo A (2019). Adaptive learning under expected and unexpected uncertainty. Nature Reviews Neuroscience, 20(10), 635-644
Farashahi S, Rowe K, Aslami Z, Lee D, Soltani A (2017). Feature-based Learning Improves Adaptability without Compromising Precision. Nature Communications, 8:1768.
Farashahi S, Donahue CH, Khorsand P, Seo H, Lee D, and Soltani A (2017). Metaplasticity as a Neural Substrate for Adaptive Learning Choice under Uncertainty. Neuron, 94(2), 401–414
Soltani A, Khorsand P, Guo CZ, Farashahi S, Liu J (2016). Neural Substrates of Cognitive Biases during Probabilistic Inference. Nature Communications, 7:11393
Soltani A, Noudoost B, Moore T (2013). Dissociable Dopaminergic Control of Saccadic Target Selection and its Implications for Reward Modulation. Proceedings of the National Academy of Sciences, 110 (9): 3579-84
Hunt LT, Kolling N, Soltani A, Woolrich MW, Rushworth MFS, Behrens TEJ (2012). Mechanisms Underlying Cortical Activity During Value-guided Choice. Nature Neuroscience, 15(3): 470-476
Soltani A, De Martino B, Camerer C (2012). A Range-normalization Model of Context-dependent Choice: A New Model and Evidence. PLoS Computational Biology, 8(7): e1002607
Soltani A, Wang X-J (2010). Synaptic Computation Underlying Probabilistic Inference. Nature Neuroscience, 13(1): 112-119
Soltani A, Koch C (2010). Visual Saliency Computations: Mechanisms, Constraints, and the Effect of Feedback. Journal of Neuroscience, 30(38): 12831-43
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