Emily Mackevicius

Papers

  • TS Okubo, EL Mackevicius, HL Payne, GF Lynch, and MS Fee. Growth and splitting of neural sequences in songbird vocal development. Nature (in press, 2015). Modeling code available here.
  • TS Okubo, EL Mackevicius, and MS Fee. In vivo recording of single-unit activity during singing in zebra finches. Cold Spring Harbor Protocols, October 23, 2014. 2014(12):1273-83.
  • EL Mackevicius, MD Best, HP Saal, and SJ Bensmaia. Millisecond precision spike timing shapes tactile perception. Journal of Neuroscience, October 31, 2012. 32(44):15309-15317.
  • Videos

  • I made Squid skin with a mind of its own to demonstrate how beautiful and complex behavior can emerge from simple rules.
  • I made several other videos as part of MIT-K12 video outreach project, and their partnership with Khan Academy: Bread mold kills bacteria; 2D equilibrium -- balancing games; Shifts in equilibrium; Homeostasis; Seeds and early plant growth; The math behind circular motion
  • I'm featured in a video for WBUR's Brain Matters series talking about songbird research: Learning from Songbirds
  • I lectured at the CBMM summer course on Learning from a Computational Neuroscience Perspective
  • Helpful course websites

  • Computational neuroscience is a fascinating and expanding subject. Over the past few years, I've been involved in teaching several computational neuroscience courses, including designing new curricula. Links to relevant materials (and websites for a couple of my other favorite courses) are below:
  • Tutorial series I founded in computational topics related to brain and cognitive sciences. Exercises, references and videos are posted.
  • Methods in Computational Neuroscience, a Woods Hole summer course I TAed.
  • The Center for Brains, Minds and Machines Woods Hole summer course, including my lecture on Learning from a Computational Neuroscience Perspective.
  • Intro to Neural Computation (9.40), a MIT undergrad course I TAed and helped design.
  • Statistical learning theory and applications (taught by Tomaso Poggio) has helpful slides and lecture notes on the theory and algorithms involved in machine learning.
  • How to make (almost) anything (taught by Neil Gershenfeld) provides detailed practical advice on making (almost) anything. It's also fun to look through what people made each week. Here's my page documenting each of my weekly projects.
  • Other