Publications
Lipton M.H., Park S., Dadarlat M.C.*, (2026). Population-level encoding of somatosensation in mouse sensorimotor cortex. Journal of Neurophysiology. Article in press. doi: 10.1152/jn.00005.2026.
Senneka S.J., Dadarlat M.C.*, (2026). Integration of learned artificial sensation with vision during freely moving navigation. Proceedings of the National Academy of Sciences. 123 (9), e2521769123. doi: 10.1073/pnas.2521769123.
Park S., Lipton M., Dadarlat M.C.*, (2024). Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning. Journal of Neural Engineering. 21(6). doi: 10.1088/1741-2552/ad83c0.
Park S., Lipton M., Sun Y.J., Dadarlat M.C.*, (2024). Protocol for recording neural activity evoked by electrical stimulation in mice using two-photon calcium imaging. STAR Protocols. 5(2):103027. doi: 10.1016/j.xpro.2024.103027.
Dadarlat M.C.*, Sun Y.J., Stryker M.P.#, (2024). Activity-dependent recruitment of inhibition and excitation in the awake mammalian cortex during electrical stimulation. Neuron. 112 (5), p821-834.e4. doi: 10.1016/j.neuron.2023.11.022.
Dadarlat M.C., Canfield R.A., Orsborn A.L., (2023). Neural Plasticity in Sensorimotor Brain-Machine Interfaces. Annual Reviews of Biomedical Engineering. 25:51-76. doi: 10.1146/annurev-bioeng-110220-110833.
Dyballa L., Hoseini M.S., Dadarlat M.C., Zucker S.W., Stryker M.P.#, (2018). Flow stimuli reveal ecologically appropriate responses in mouse visual cortex. Proceedings of the National Academy of Sciences. 115(44):11304-11309. doi: 10.1073/pnas.181126511.
Dadarlat M.C., Stryker M.P.#, (2017). Locomotion enhances neural encoding of visual stimuli in mouse V1. Journal of Neuroscience. 37(14): 3764-3775. doi: 10.1523/JNEUROSCI.2728-16.2017.
Dadarlat M.C., Sabes P.N.#*, (2016). Encoding and Decoding of Multi-Channel ICMS in Macaque Somatosensory Cortex. Institute of Electrical and Electronics Engineers Transactions on Haptics, 9(4):508-514. doi: 10.1109/TOH.2016.2616311
Dadarlat M.C., O’Doherty J.E., Sabes P.N.#*, (2014). A learning-based approach to artificial sensory feedback leads to optimal integration. Nature Neuroscience, 18:138-144. doi: 10.1038/nn.3883.
Jedlicka S.S., Dadarlat M., Hassell T., Lin Y., Young A., Zhang M., Irazoqui P., Rickus J.L., (2009). Calibration of neurotransmitter release from neural cells for therapeutic implants. International Journal of Neural Systems, 19(3):197-212. doi: 10.1142/S0129065709001963
Books and chapters in books
Park S., Lipton M., & Dadarlat M., (2024). Toward an Optical BCI: Overcoming the Limitation of Low Sampling Rate for Decoding Limb Movements. In C. Guger (Ed.), Brain-Computer Interface Research: A State-of-the-Art Summary 12 (pp. 113-122). Springer Nature.
Dadarlat M.C., O’Doherty J.E., and Sabes P.N.#, (2014). A learning-based approach to artificial sensory feedback. In C. Guger (Ed.), Brain-Computer Interface Research: A State of the Art Summary 3 (pp. 31-46). Springer Nature.
Refereed conferences
Dadarlat M.C., Sun Y., Stryker M.P.#, (2019). Widespread activation of awake mouse cortex by electrical stimulation. Institute of Electrical and Electronics Engineers Engineering Medicine and Biology Society International Conference on Neural Engineering. 2019:1113–1117. doi: 10.1109/NER.2019.8716956.
Dadarlat M.C., O’Doherty J, Sabes P.#, (2013). Multisensory integration of vision and intracortical microstimulation for sensory feedback. [Poster presentation] Computational and Systems Neuroscience Conference, Salt Lake City, Utah.