research_ref.md

[TOC]

data

• https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml
  • sklearn also lets you generate some nice synthetic datasets
• pmlb

misc

• anonymous github: https://anonymous.4open.science
• posters
  • new simple poster template
  • infographics don't work great
  • "perfection is not when you have nothing to add, it's when you have nothing to take away"
• draw arch
• viz architectures
• medical ideas: many diseases manifest themselves in the activity of neurons, not in the structure
• ovw

levels

• Marr's three levels
  1. computation - behavior
  2. algorithm - causal connectomics
  3. basic implementation - structural connectmoics, molecular biology

questions

• problems that are solved, or soon will be
  • how do single neurons compute?
  • what is the connectome of a small nervous system, like that of C. elegans (300 neurons)?
  • how can we image a live brain of 100,000 neurons at cellular and millisecond resolution?
    • hydra was completed
  • how does sensory transduction work?
• problems that we should be able to solve in the next 50 years
  • can we add senses to the brain?
    • like cochlear implant
    • like vibrations
  • how do circuits of neurons compute?
  • what is the complete connectome of the mouse brain (70e6 neurons)?
  • how can we image a live mouse brain at cellular and millisecond resolution?
  • what causes psychiatric and neurological illness?
  • how do learning and memory work?
    • short-term vs. long-term
    • declarative vs. non-declarative
    • encodes relationships between things not things themselves
    • memory retrieval
  • why do we sleep and dream?
    1. sleep is restorative (but then why high neural activity?)
    2. allows the brain to run simulations
    3. consolidating memories and forgetting
  • where is consciousness?
    • at this point, sounds and vision should line up (delayed appropriately)
  • how do we make decisions?
  • how does the brain represent abstract ideas?
  • what does neural baseline activity represent?
  • how does the brain solve timing?
    • moving eyes
    • blinking
    • hearing and vision time differences
  • how does sensorimotor learning build a model of the world?
• problems that we should be able to solve, but who knows when
  • how do brains simulate the future?
  • how does the mouse brain compute?
  • what is the complete connectome of the human brain (8e10 neurons)?
  • how can we image a live human brain at cellular and millisecond resolution?
  • how could we cure psychiatric and neurological diseases?
  • how could we make everybody’s brain function best?
  • brain and quantum?
    • some work in quantum neural nets
  • how is info coded in neural activity?
    • like measuring tansistors and guessing what computer is doing
    • neuron gets lots of inputs
  • do glial cells and other signaling molecules compute?
  • what is intelligence?
    • what is iq?
  • how do specialized systems integrate?
• problems we may never solve
  • what are emotions?
    • brain states that quickly assign values
    • in the amygdala
  • how does the human brain compute?
  • how can cognition be so flexible and generative?
  • how and why does conscious experience arise?
    • thing that flickers on when you wake up that was not there
    • evolutionary to manage all the different systems
• meta-questions
  • what counts as an explanation of how the brain works? (and which disciplines would be needed to provide it?)
  • how could we build a brain? (how do evolution and development do it?)
  • what are the different ways of understanding the brain? (what is function, algorithm, implementation?)
• ref David Eaglemen article: http://discovermagazine.com/2007/aug/unsolved-brain-mysteries
• ref Adolphs 2015, "The unsolved problems of neuroscience"

small misc things

• neuroscience is like IT - given computer, figure out how it works
• https://grand-challenge.org/all_challenges/

rna barcoding

• allows for tagging different neurons
  • can then optically get differences
  • also can sequence and get differences (http://www.cell.com/neuron/pdf/S0896-6273%2816%2930421-4.pdf)
• future of electrophysiology: https://www.technologynetworks.com/neuroscience/articles/shining-a-light-on-the-future-of-electrophysiology-286992

brain transplant

• computational hypothesis of the mind

tms

• temporary cure for autism
• can change people's minds

quantum brain

• quantum brain?

brain on a chip

• neuromorphic chips
• grow cells in vivo

connectomics

• C Elegans
  • 302 neurons
  • no evidence of Hebbian learning
  • develop synaptogenesis rules?

history

• biology U: phenomenon (high level) -> element (low level) -> synthesis (high level)

cogsci

• model-free vs model-based

biomechanics

• brain exists to make suggestions to motor system
• reflexes don't need cortex, but still tricky
  • ex. wipe skin when irritated in frog - works when leg at different points, leg stopped
• idea: t-sne on neural dynamics: someone at Emory
• spinal chord is where reflexes are, then brainstem, and cortex is quite slow

scientists

• ml
  • Geoffrey Hinton - emeritus, Toronto
    • Yann Lecun (NYU) - heads fb AI
      • was his research associate
  • Michael Jordan - Berkeley
    • students: Ng, Blei; postdoc: Bengio
    • Andrew Ng - Stanford
    • David Blei - topic modeling
    • Yoshua Bengio - McGill
      • Ian Goodfellow - GANs
  • Jeff Hawkins - established redwood
    • left to found numenta
  • Terry Sejnowski
    • coinvented Boltzmann machines
  • Daphne Koller - co-founder of Coursera
    • representation, inference, learning, and decision making
  • Schmidhuber & Hochreiter - LSTM
  • Jitendra Malik - computer vision
  • Andrej Karpathy - blogger, Tesla AI director
• comp neuro
  • Karl Friston - functional imaging analysis
  • Raymond Dolan - emotion, pain
  • Terrence J. Sejnowski - boltzmann machines, ICA
  • david marr - vision
  • tomaso poggio - vision
  • Christoph Koch - head of allen institute
  • Daniel Wolpert - noise in the nervous system
  • Jonathan Cohen - theory
  • Larry Abbott - theoretical neuroscience
  • György Buzsáki - oscillations
  • Peter Dayan
  • Haim Sompolinsky
  • Stephen Grossberg
  • Randall C. O'Reilly
  • Nancy Kopell
  • Chris Eliasmith
  • Michael Hasselmo
  • David Heeger
  • Roger D. Traub
  • Bard Ermentrout
  • Eugene M. Izhikevich
  • Eric L. Schwartz
• misc
  • Byron Yu - bmi
  • James DiCarlo
  • Liam Paninski - decoding
  • Jack Gallant - v4, fmri
  • Bin Yu - model consistency
  • Sebastian Seung - connectomes
  • Surya Ganguli - Stanford
  • David Cox - MIT/IBM
  • Jascha Sohl-Dickstein - Google
  • Iain Couzin
  • Haim Sompolinksy
  • charlest gilbert - rockefeller - studies spatial distribution of visual cortex
  • Susumu Tonegawa - memory