Dr. Ran Darshan
Research work
The focus of the lab is Theoretical and Computational Neuroscience. We utilize a wide range of tools and concepts from various fields, such as statistical mechanics, dynamical systems theory, machine learning and control theory in our quest to understand how cognitive abilities emerge from collective neural activity and how such activity evolves during the learning process.
Areas of interest & scientific knowledge
Cognitive Neuroscience
Computational & Theoretical Neuroscience
System & Physiological Neuroscience
Methodology
Selected Publications
- Kim, C. M., Finkelstein, A., Chow, C. C., Svoboda, K., & Darshan, R. (2023). Distributing task-related neural activity across a cortical network through task-independent connections. Nature Communications https://www.nature.com/articles/s41467-023-38529-y
- Rajagopalan, A., Darshan, R., Hibbard, K., Fitzgerald, J. E., & Turner, G. C. (2023). Reward expectations direct learning and drive operant matching in Drosophila. Proceedings of the National Academy of Science https://www.pnas.org/doi/abs/10.1073/pnas.2221415120?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed
- Darshan, R., & Rivkind, A. (2022). Learning to represent continuous variables in heterogeneous neural networks. Cell Reports https://www.cell.com/cell-reports/pdfExtended/S2211-1247(22)00360-6
- Darshan, R., van Vreeswijk, C. & Hansel, D. (2018). Strength of Correlations in Strongly Recurrent Neuronal Networks. Physical Review X
https://journals.aps.org/prx/pdf/10.1103/PhysRevX.8.031072 - Darshan, R., Wood, W.E ., Peters, S., Leblois, A. & Hansel, D. (2017). A canonical neural mechanism for behavioral variability. Nature Communications
https://www.nature.com/articles/ncomms15415
