ovw_dl_for_neuro.md

layout	title	category
notes	dl for neuro	research

{:toc}

Ideas for neuroscience using deep learning

list of comparisons: https://docs.google.com/document/d/1qil2ylAnw6XrHPymYjKKYNDJn2qZQYA_Qg2_ijl-MaQ/edit

Modern deep learning evokes many parallels with the human brain. Here, we explore how these two concepts are related and how deep learning can help understand neural systems using big data.

• https://medium.com/the-spike/a-neural-data-science-how-and-why-d7e3969086f2

Brief history

The history of deep learning is intimately linked with neuroscience, with the modern idea of convolutional neural networks dates back to the necognitron.

pro big-data

Artificial neural networks can compute in several different ways. There is some evidence in the visual system that neurons in higher layers of visual areas can, to some extent, be predicted linearly by higher layers of deep networks. However, this certainly isn't true in general.

• when comparing energy-efficiency, must normalize network performance by energy / number of computations / parameters

anti big-data

• could neuroscientist understand microprocessor
• no canonical microcircuit

Data types

	EEG	ECoG	Local Field potential (LFP) -> microelectrode array	single-unit	calcium imaging	fMRI
scale	high	high	low	tiny	low	high
spatial res	very low	low	mid-low	x	low	mid-low
temporal res	mid-high	high	high	super high	high	very low
invasiveness	non	yes (under skull)	very	very	non	non

• ovw of advancements in neuroengineering
• cellular
  • extracellular microeelectrodes
  • intracellular microelectrode
  • neuropixels
• optical
  • calcium imaging / fluorescence imaging
  • whole-brain light sheet imaging
  • voltage-sensitive dyes / voltage imaging
  • adaptive optics
  • fNRIS - like fMRI but cheaper, allows more immobility, slightly worse spatial res
  • oct - noninvasive - can look at retina (maybe find biomarkers of alzheimer's)
  • fiber photometry - optical fiber implanted delivers excitation light
• alteration
  • optogenetic stimulation
  • tms
    • genetically-targeted tms: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4846560/
  • local microstimulation with invasive electrodes
• high-level
  • EEG/ECoG
  • MEG
  • fMRI/PET
    • molecular fmri (bartelle)
  • MRS
  • event-related optical signal = near-infrared spectroscopy
• implantable
  • neural dust

general projects

• could a neuroscientist understand a deep neural network? - use neural tracing to build up wiring diagram / function
• prediction-driven dimensionality reduction
• deep heuristic for model-building
• joint prediction of different input/output relationships
• joint prediction of neurons from other areas

datasets

• non-human primate optogenetics datasets

• vision dsets

  • MRNet: knee MRI diagnosis

• datalad lots of stuff

• springer 10k calcium imaging recording: https://figshare.com/articles/Recordings_of_ten_thousand_neurons_in_visual_cortex_during_spontaneous_behaviors/6163622

  • springer 2: 10k neurons with 2800 images

  • stringer et al. data

  • 10000 neurons from visual cortex

• neuropixels probes

  • 10k neurons visual coding from allen institute
  • this probe has also been used in macaques

• allen institute calcium imaging

  • An experiment is the unique combination of one mouse, one imaging depth (e.g. 175 um from surface of cortex), and one visual area (e.g. “Anterolateral visual area” or “VISal”)

• predicting running, facial cues

  • dimensionality reduction
    • enforcing bottleneck in the deep model
    • how else to do dim reduction?

• responses to 2800 images

• overview: http://www.scholarpedia.org/article/Encyclopedia_of_computational_neuroscience

• keeping up to date: https://sanjayankur31.github.io/planet-neuroscience/

• lots of good data: http://home.earthlink.net/~perlewitz/index.html

• connectome

  • fly brain: http://temca2data.org/

• models

  • senseLab: https://senselab.med.yale.edu/
    • modelDB - has NEURON code
  • model databases: http://www.cnsorg.org/model-database
  • comp neuro databases: http://home.earthlink.net/~perlewitz/database.html

• raw misc data

  • crcns data: http://crcns.org/
    • visual cortex data (gallant)
    • hippocampus spike trains
  • allen brain atlas: http://www.brain-map.org/
    • includes calcium-imaging dataset: http://help.brain-map.org/display/observatory/Data+-+Visual+Coding
  • wikipedia page: https://en.wikipedia.org/wiki/List_of_neuroscience_databases

• human fMRI datasets: https://docs.google.com/document/d/1bRqfcJOV7U4f-aa3h8yPBjYQoLXYLLgeY6_af_N2CTM/edit

• Kay et al 2008 has data on responses to images

• calcium imaging for spike sorting: http://spikefinder.codeneuro.org/

  • spikes: http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/software

<script type="text/bibliography"> @article{hubel1962receptive, title={Receptive fields, binocular interaction and functional architecture in the cat's visual cortex}, author={Hubel, David H and Wiesel, Torsten N}, journal={The Journal of physiology}, volume={160}, number={1}, pages={106--154}, year={1962}, publisher={Wiley Online Library}, url={http://onlinelibrary.wiley.com/wol1/doi/10.1113/jphysiol.1962.sp006837/abstract} } @article{singh2017consensus, title={A consensus layer V pyramidal neuron can sustain interpulse-interval coding}, author={Singh, Chandan and Levy, William B}, journal={PloS one}, volume={12}, number={7}, pages={e0180839}, year={2017}, publisher={Public Library of Science}, url={http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180839} } @article{herz2006modeling, title={Modeling single-neuron dynamics and computations: a balance of detail and abstraction}, author={Herz, Andreas VM and Gollisch, Tim and Machens, Christian K and Jaeger, Dieter}, journal={science}, volume={314}, number={5796}, pages={80--85}, year={2006}, publisher={American Association for the Advancement of Science}, url={http://science.sciencemag.org/content/314/5796/80.long} } @article{carandini2004amplification, title={Amplification of trial-to-trial response variability by neurons in visual cortex}, author={Carandini, Matteo}, journal={PLoS biology}, volume={2}, number={9}, pages={e264}, year={2004}, publisher={Public Library of Science}, url={http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0020264} } @article{yamins2014performance, title={Performance-optimized hierarchical models predict neural responses in higher visual cortex}, author={Yamins, Daniel LK and Hong, Ha and Cadieu, Charles F and Solomon, Ethan A and Seibert, Darren and DiCarlo, James J}, journal={Proceedings of the National Academy of Sciences}, volume={111}, number={23}, pages={8619--8624}, year={2014}, publisher={National Acad Sciences}, url={http://www.pnas.org/content/111/23/8619} } @incollection{fukushima1982neocognitron, title={Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition}, author={Fukushima, Kunihiko and Miyake, Sei}, booktitle={Competition and cooperation in neural nets}, pages={267--285}, year={1982}, publisher={Springer} } @article{marr1976understanding, title={From understanding computation to understanding neural circuitry}, author={Marr, David and Poggio, Tomaso}, year={1976}, url={https://dspace.mit.edu/handle/1721.1/5782} } @article{schuman2017survey, title={A survey of neuromorphic computing and neural networks in hardware}, author={Schuman, Catherine D and Potok, Thomas E and Patton, Robert M and Birdwell, J Douglas and Dean, Mark E and Rose, Garrett S and Plank, James S}, journal={arXiv preprint arXiv:1705.06963}, year={2017}, url={https://arxiv.org/abs/1705.06963} } </script>