With a background in Electrical and Biomedical Engineering, I completed my PhD in Computational Neuroscience at Dartmouth College where I studied the computational principles and neural mechanisms of Reinforcement Learning in the brain. After finishing my PhD, I joined Flatiron Institute to study the connection between biologically inspired neural networks and Representation Learning in the brain. Currently, I am a Senior machine learning engineer at Harbinger Health, Inc. a bioplatform company focused on detection of early cancer. You can find more about me here. Please contact me via:
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9. Qin S, Farashahi S, Lipshutz D, Sengupta A, Chklovskii D & Pehlevan C (2023). Coordinated drift of receptive fields in
Hebbian/anti-Hebbian network models during noisy representation learning. Nature
Neuroscience, 1-11.
8. Farashahi S, & Soltani A (2021). Computational mechanisms of distributed value
representations and learning strategies. Nature
Communications 12, 7191.
7. Friedrich J, Golkar S, Farashahi S, Genkin A, Sengupta A & Chklovskii D (2021). Neural optimal
feedback control with local learning rules. Advances in Neural Information Processing Systems 34.
6. Farashahi S, Donahue CH, Hayden BY, Lee D, &
Soltani A (2019). Flexible combination of reward information across primates.
Nature Human Behavior 3(11), 1215-1224.
5. Farashahi S, Azab H, Hayden B, & Soltani A (2018). On the flexibility
of basic risk attitudes in monkeys. Journal of Neuroscience 38(18), 4383-4398.
4. Farashahi S, Rowe K, Aslami Z, Lee D, & Soltani A
(2017). Feature-based learning improves adaptability without
compromising precision. Nature Communications 8(1), 1-16.
3. Farashahi S, Donahue CH, Khorsand P, Seo
H, Lee D, & Soltani A (2017). Metaplasticity as a
neural substrate for adaptive learning and choice under uncertainty. Neuron 94(2), 401-414.
2. Bahrami F, & Farashahi S, (2017). How Do We
Navigate Our Way to Places?. Computational Models of Brain and Behavior, 357-372.
1. Soltani A, Khorsand P, Guo C, Farashahi S, & Liu J (2016). Neural Substrates of Cognitive Biases during
Probabilistic Inference. Nature Communications 7(1), 1-14.