One of the central questions in neuroscience is how the nervous system generates and regulates behavior. To understand the neural code for any behavior, an ideal experiment would entail (i) quantitatively defining that behavior, (ii) recording neuronal activity in relevant brain regions to identify the underlying neuronal circuits and eventually (iii) manipulating them to test their function. Novel methods in neuroscience have greatly advanced our abilities to conduct such experiments but are still insufficient. In this thesis, I developed methods for these three goals. In Chapter 2, I describe an automatic behavior identification and classification method for the cnidarian Hydra vulgaris using machine learning. In Chapter 3, I describe a fast volumetric two-photon microscope with dual-color laser excitation that can image in 3D the activity of populations of neurons from visual cortex of awake mice. In Chapter 4, I present a machine learning method that identifies cortical ensembles and pattern completion neurons in mouse visual cortex, using two-photon calcium imaging data. These methods advance current technologies, providing opportunities for new discoveries.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-xn2z-b714 |
| Date | January 2019 |
| Creators | Han, Shuting |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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