Course on neuro-tech

This semester (spring, 2017), I taught a course titled “New breakthrough technologies in neuroscience research”:

Catalog description: In the past decade, neuroscientists have developed a number of new, exciting methods for studying the brain. For examples, some research labs now use laser to activate genetically-engineered neurons (optogenetics). Others have created a detailed 3-dimensional map of neural connectivity at nano-scale resolution, based on electron microscopy images (connectome). Some other labs are developing neuroprosthetic devices that can be controlled directly by neural activity (brain-computer interface). In this course, we will learn about these breakthrough technologies, by reading primary research articles.

Students read the following papers (chosen by me):

  • optogenetics: “Millisecond-timescale, genetically targetted optical control of neural activity” by Boyden et al. (2005)
  • connectome: “Connectomic reconstruction of the inner plexiform layer in the mouse retina” by Helmstaedter et al., (2013); “Space-time wiring specificity support direction selectivity in the retina” by Kim et al., (2014)
  • Brain-Computer Interface: “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm” by Hochberg et al., (2012)

Students made presentations on the following neuro-tech:

  • Dopamine nanosensor: “High-resolution imaging of cellular dopamine efflux using a fluorescent nanosensor array”
  • Brainbow: “Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system”
  • Neuronal Positioning System: “Multispectral labeling technique to map many neighboring axonal projections in the same tissue”
  • BCI: “A high-performance brain-computer interface”
  • Retinal implant: “Photovoltaic restoration of sight with high visual acuity”
  • Photoacoustic imaging: “Noninvasive photoacoustic angiography of animal brains with near-infrared light and an optical contrast agent”
  • Gamma Knife: “The Leksell Gamma Knife Perfexion and comparisons with its predecessors”

Learning to extract “big ideas” from these research articles definitely takes some practice and hard work.

Python for Matlab users

Found several resources on Python for Matlab users.

  • xkcd: Python
  • (from scipy) https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html
  • (Nice table) http://mathesaurus.sourceforge.net/matlab-numpy.html
  • (Blog) http://bastibe.de/2013-01-20-a-python-primer-for-matlab-users.html
  • (Blog) http://stsievert.com/blog/2015/09/01/matlab-to-python/
  • (Presentation): http://brain.cin.ucsf.edu/~craig/rstuff/Python_for_Matlab_Users.pdf

Data visualization skills

Some thoughts on teaching data visualization skills.

  • Need to go beyond learning to use a piece of software, so that students develop an appreciation (an “eye”) for effective communication. It would be great to collaborate with visual arts department.
  • Emphasis on removing noise/clutter and enhancing signal (i.e., increasing signal to noise ratio).
  • It is a part of general communication skills. The same principle applies to making slides or diagrams.
  • Students need to learn a few basic forms (scatter plot, bar graph, histogram).
  • They also need to realize they have control over the plotting elements (scales, labels, marker size/colors, thickness, tick marks, etc.), and learn a few simple things that enhances clarity.
  • Initial target? students doing summer research or honors thesis projects.

A few resources:

Shannon Lecture by Friend

There was a Shannon Luminary Lecture by Stephen Friend at the Nokia Bell Lab at Murray Hill. I thought it was going to be mostly on genetics and data science, but it was (pleasantly) much more. There were many thought-provoking points:

  • “endangered experiences”: pausing to have a deep dialogue with yourself.
  • “we may have knowledge of the past but cannot control it; we may control the future but have no knowledge of it (Shannon)”: what about the present?
  • Books: “Sapiens” and “Homo Deus” and “Inevitable”
  • “nature vs. nurture”: This is a useful framework, but too oversimplified.
  • agency and free will: Presenting assessment is not sufficient. I agree that most students would not step up to studying more/better, even when presented with very low quiz/exam scores. We also need to appeal to the subconscious mind (not just rational consciousness), in order to motivate.
  • Thinking upside-down: Study the resilience, in addition to the disease.
  • Binary vs. spectrum.
  • many more…

It is a privilege to live/work so close to this place with rich history of innovation.

Movie Arrival

I finally watched the movie Arrival (2016) with some students today. The original story by Ted Chiang is cleverer and more thought-provoking. Nevertheless, it was fun to see the scientists in the movie working with Mathematica and Matlab. In his blog, Stephen Wolfram wrote about his involvement with this movie.

Coincidentally, there was an neuroscience news today about how language might shape our perception of time. Thanks to JP for these links.