EL Mackevicius, MTL Happ, MS Fee.
An avian cortical circuit for chunking tutor song syllables into simple vocal-motor units. Nature Communications 2020. Data available here.
EL Mackevicius, AH Bahle, AH Williams, S Gu, NI Denissenko, MS Goldman, MS Fee.
Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. eLife 2019. Code available here.
EL Mackevicius and MS Fee.
Building a state space for song learning. Current Opinion in Neurobiology 2018.
TS Okubo, EL Mackevicius, HL Payne, GF Lynch, and MS Fee.
Growth and splitting of neural sequences in songbird vocal
development. Nature 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,
EL Mackevicius, MD Best, HP Saal, and SJ Bensmaia.
precision spike timing shapes tactile perception. Journal of Neuroscience, October 31, 2012. 32(44):15309-15317.
COSYNE talk, tutorial, and Simons Collaboration on the Global Brain: article
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:
mold kills bacteria;
-- balancing games;
and early plant growth;
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
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
Exercises, references and videos
Methods in Computational
Neuroscience, a Woods Hole summer course I TAed.
Center for Brains, Minds and
Machines Woods Hole summer course, including my lecture on
Learning from a Computational
Neural Computation (9.40), a MIT undergrad course I TAed and
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.
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