NEUS1400 Data Science Applications in Neuroscience (1–10 cr)

The applications of data science will be grouped into several neuroscience sub-fields:

1. Time series analysis of continuous signals derived from physiological, such as EEG
(electroencephalography) and behavioral, such as accelerometer, data.

2. Image processing of structural data on multiple scales ranging from EM (electron microscope)
to MRI (Magnetic resonance imaging).

3. Image processing of functional data, primarily fMRI.

4. Natural language processing (NLP) usage in linguistics and cognitive psychology.

5. Massive spike train analysis from sources such as calcium imaging and multi-electrode arrays.

6. Behavior analysis from video streams.

The seminar will introduce the frontiers of the scientific research which applies data science approaches
to the processing of data related to neuroscience. The seminar will handle the different sub-fields of
neuroscience in which data science approaches have been implemented and will discuss recent
developments in those fields.

Berman GJ, Choi DM, Bialek W, Shaevitz JW. Mapping the stereotyped behaviour of freely
moving fruit flies. J Royal Society Interface. 2014;11(99):20140672.

2. Calhoun AJ, Pillow JW, Murthy M. Unsupervised identification of the internal states that shape
natural behavior. Nature Neuroscience. 2019;22(12):2040‐2049.

3. Jonas E, Kording KP. Could a Neuroscientist Understand a Microprocessor?. PLoS Computational
Biology. 2017;13(1):e1005268.

4. Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis-theory
cycle in large-scale neuroscience. Current Opinions in Neurobiology. 2018;50:232‐241.

5. Todd JG, Kain JS, de Bivort BL. Systematic exploration of unsupervised methods for mapping
behavior. Physical Biology. 2017;14(1):015002.

etc.