Maria Dadarlat

Maria Dadarlat Profile Picture

Dr. Maria Dadarlat
University of California, San Francisco 

Contact Info:
BME 2031

Training Group(s):
Computational and Systems Biology
Integrative Neuroscience

Active Mentor - currently hosting PULSe students for laboratory rotations and recruiting PULSe students into the laboratory; serves on preliminary exam committees

Current Research Interests:

Humans make highly precise movements by combining information from various sensory modalities to plan and execute motor commands, a process called sensorimotor integration that must be learned through experience. We do so by forming internal models, dynamic neural maps between sensory information and motor commands. Despite evidence of the existence of such models, we have a limited understanding of how internal models form. The Dadarlat lab studies learning in the sensorimotor system by exposing adult animals to a novel sensorimotor pairing: using electrical stimulation to encode artificial sensory feedback during a behavioral task and state-of-the-art neural recording and mesoscopic 2-photon imaging to record changes in neural coding across sensory, parietal, and motor cortex during learning. In addition to systems neuroscience, the Dadarlat lab focuses on the development of artificial sensory feedback for neural prostheses, and approaches to enhance adult neuroplasticity to promote recovery from neural injury and disease.

Selected Publications:

  • Dadarlat MC, Sun Y, Stryker MP. Multiphoton imaging in alert mouse visual cortex reveals sparse, distributed activation of neurons by electrical stimulation. In progress.
    Dadarlat MC, Sun Y, Stryker MP. (2019) Widespread activation of awake mouse cortex by electrical stimulation. Int IEEE EMBS Conf Neural Eng. 2019 Mar; 2019:1113-1117.
    Dyballa L, Hoseini MS, Dadarlat MC, Zucker SW, Stryker MP. (2018) Flow stimuli reveal ecologically appropriate responses in mouse visual cortex. PNAS 115(44):11304-11309.
    Dadarlat MC, Stryker MP. (2017) Locomotion enhances neural encoding of visual stimuli in mouse V1. Journal of Neuroscience, 37(14): 3764-3775.
    Dadarlat MC, Sabes PN. (2016) Encoding and Decoding of Multi-Channel ICMS in Macaque Somatosensory Cortex. IEEE Transactions on Haptics, 9(4):508-514.
    Dadarlat MC, O’Doherty JE, Sabes PN. (2014) A learning-based approach to artificial sensory feedback leads to optimal integration. Nature Neuroscience, 18:138-144.
  • Faculty Profile

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