I work on how the brain turns messy visual input into fast, reliable decisions about the objects in the world. My work focuses on the coding of object features in visual cortex and how neural variability, attention, and task-relevant information link those features to behavior. My recent work uses electrophysiology, psychophysics, and computational modeling to examine how multiple object features are jointly encoded and read out from visual cortex to guide decisions. These insights translate directly to building smarter vision systems, improving human–AI interaction, and designing technologies that make robust decisions under uncertainty.

Post-doc Lab: Cohen Lab at Neuroscience Institute, UChicago.

Ph.D. Labs: Connor and Nielsen Labs at MBI, Johns Hopkins

Preprints:

• Ruff, D.A., Sheets, D.E.G., Srinath, R., Diniz, G.B., Griggs, D.J., Beckman, D., Ott, S., Schwartz, K., Erices, C., Muller, S., et al. (2026). Loss of neuronal population organization links pathology to behavior in a model of Alzheimer’s disease. https://doi.org/10.64898/2026.03.18.712735.

• Srinath, R., Xu, Y., Ruff, D.A., Ni, A.M., Doiron, B., and Cohen, M.R. (2025). Guided by Noise: Correlated Variability Channels Task-Relevant Information in Sensory Neurons. Preprint at bioRxiv, https://doi.org/10.1101/2025.08.13.669902 

• Srinath, R., Czarnik, M.M., and Cohen, M.R. (2024). Coordinated Response Modulations Enable Flexible Use of Visual Information. Preprint at bioRxiv, https://doi.org/10.1101/2024.07.10.602774. (updated title) The Shape of Flexibility: How Visual Neurons Mediate Visual and Behavioral Generalization"

Publications:

• Srinath, R.*, Ni, A.M.*, Marucci, C., Cohen, M.R., and Brainard, D.H. (2025). Orthogonal neural representations support perceptual judgments of natural stimuli. Sci. Rep. 15, 5316. 10.1038/s41598-025-88910-8. (* = equal contribution)

• Emonds, A.M., Srinath, R., Nielsen, K.J., and Connor, C.E. (2023). Object representation in a gravitational reference frame. eLife 12, e81701. 10.7554/eLife.81701.

• Srinath, R., Emonds, A., Wang, Q., Lempel, A.A., Dunn-Weiss, E., Connor, C.E., and Nielsen, K.J. (2021). Early Emergence of Solid Shape Coding in Natural and Deep Network Vision. Curr. Biol. 31, 51-65.e5. 10.1016/j.cub.2020.09.076. 

  • Dispatch: Leopold, D.A., and Afraz, A. (2021). Neurophysiology: The Three-Dimensional Building Blocks of Object Vision. Curr. Biol. 31, R9–R11. https://doi.org/10.1016/j.cub.2020.10.064.

• Srinath, R., Ruff, D.A., and Cohen, M.R. (2021). Attention improves information flow between neuronal populations without changing the communication subspace. Curr. Biol. 31, 5299-5313.e4. 10.1016/j.cub.2021.09.076.

• Srinath, R., and Ray, S. (2014). Effect of amplitude correlations on coherence in the local field potential. J. Neurophysiol. 112, 741–751. 10.1152/jn.00851.2013.