5 ms·

just curious - how do neuroscience people and people of non-CS fields find out about HN?

mattkrause 8y ago
A lot of bio stuff now generates huge amounts of data and needs computational skills to collect and interpret. My undergrad degree was in CS and I still split my time between training animals and training models (plus writing and, today, RAID-babysitting). We get about 1 Gb/min of neurophysiological data (x4-6 hours/day) and I'm hoping to scale that up quite a bit soon. People doing microscopy also generate giant datasets, as do the sequencing folks. Me, specifically? A labmate showed me and said "It's cool...and a timesink."
- codekilla 8y ago
  what sorts of tools are you using to deal with this data?
  - mattkrause 8y ago
    It's a hodgepodge. Mostly Matlab, Python, and R, with a few things that have tight time/memory requirements in C++. Matlab was really popular in neuroscience for a very long time, so we still have a lot of code in m-files, but most labs are moving towards Python (and a few towards R). The code quality varies a lot. Some of our "core" stuff is great, but there's also a lot of stuff that was written quickly and meant to be run once ("let's just try it"), which is cold comfort when you find it years later. People also come in with varying levels of programming skill. I'm going to try to do actual code reviews with the undergrads this summer to see if we can't make our stuff a little less embarrassing.
    - codekilla 8y ago
      I'm interested in using kdb+/q for applications dealing with large scale scientific data. Do you think there would be much interest in the neuroscience community? I'll add, I work in a lab and have seen the stuff of nightmares myself.
      - mattkrause 8y ago
        Possibly! I've not played with the time series databases much. Most of our queries would be something like "give me signals from channels A-C that are +/- 1 second from all events of type X." If it would help with something like that, it'd be great.
    - codekilla 8y ago
      I can't reply to your last comment...but yes..anything dealing with time series it's easily the best in the world. In a past life long ago I worked in finance and heard about it then. Recently, I was thinking about simulations and how to maybe deal with and analyze the timeseries data and I thought about it. Apparently, NASA is experimenting with it at their Ames Frontier Development Lab, astrophysics data is huge. Anyway, I am just kicking the tires at this point and trying to find ways to implement and play around with it a bit. If you or anyone you know develops an interest, feel free to drop me a line. grantroy _@_ caltech dot edu
- loamseed 8y ago
  Are there publicly available repositories of this kind of data? Where? What are the best modern methods to analyze them?
  - mattkrause 8y ago
    Yup. The neuroimaging folks are really good about sharing data. If you want to look at MRI/MEG/EEG data, https://openneuro.org https://openneuro.org might be a good place to start. CRCNS (https://crcns.org/data-sets https://crcns.org/data-sets) has some neurophysiology data (i.e., from implanted or inserted electrodes). This sort of data is shared a little less often, in part because it's often acquired and stored in weird, homebrew formats, though that's slowly changing. ModelDB (https://senselab.med.yale.edu/ModelDB/ https://senselab.med.yale.edu/ModelDB/) has a large collection of computational models. These are mostly biophysical models, though there's some other stuff in there too. Depending on what you're looking for, there are other more specialized repositories. NDCT (https://data-archive.nimh.nih.gov/ndct https://data-archive.nimh.nih.gov/ndct) has mental-health related clinical trial data, though you'll have to do some paperwork if you want subject-level data, which is fairly common for clinical data. MIT has a collection of eye movement data sets: http://saliency.mit.edu/datasets.html http://saliency.mit.edu/datasets.html As for the best methods, this comment box is far too small to contain all my thoughts on that :-) It depends on your question and experiment. Sometimes, all you really need is a t-test (or the randomized version), but that requires getting the experiment just right. Other times, you might need a morass of signal processing and dimensionality reduction, fed into some giant multi-level Bayesian model in a vain attempt controls for all the stuff you neglected when you designed the damn experiment. Happy to send you some pointers if you have something specific in mind though! Physics has historically had a huge leg up on the other sciences because they had real models that made testable, quantitative predictions. We're finally starting to learn enough about the brain that we can do this for neural data too, and I'm really excited about that!
    - loamseed 8y ago
      Thank you for this wealth of information! I will definitely look through, and after digging around a bit I'd love to get some pointers. I have been curious about this for a long time, but uncertain where to look, so this is very exciting. Thank you again :)
- mrep 8y ago
  Ever read the book "On Intelligence" by Jeff Hawkins and if so, what are you thoughts on it? I've read that and ray kurzweil's books and On Intelligence was my favorite and got me super interested in AI but I haven't done much with it so I'm curious what someone who has a PhD in neuroscience thinks of it.
  - beefcafe 8y ago
    Also “How to Build a Brain” by Chris Eliasmith. Interesting theories on how neurons represent functions and then build into a greater whole. I’m not a neuroscientist but my intuition says he might be getting closer to a solid mid-level (above the level of ion channels) understanding of brain function.
    - mattkrause 8y ago
      I haven't, though it's been on my list for a while. I'm collaborating with a woman who has also been working with him to model some of our data with NENGO, so I should really get around to that sooner rather than later.
- jcims 8y ago
  Just wanted to thank you for wading in here sand sharing your perspective.
  - mattkrause 8y ago
    You're welcome! Random interesting stuff is what makes the internet worth keeping around, after all :-)
    - HNLurker2 8y ago
      Many neuroscientist turn to teaching mindfulness (e.g Sam Harris) because in their spiritual journey and academic neuroscientist journey they ask : Who am I? What do you think about this? Who is the I? Where can I find it? Is it just mysticism?

A lot of bio stuff now generates huge amounts of data and needs computational skills to collect and interpret. My undergrad degree was in CS and I still split my time between training animals and training models (plus writing and, today, RAID-babysitting). We get about 1 Gb/min of neurophysiological data (x4-6 hours/day) and I'm hoping to scale that up quite a bit soon. People doing microscopy also generate giant datasets, as do the sequencing folks. Me, specifically? A labmate showed me and said "It's cool...and a timesink."

codekilla 8y ago
what sorts of tools are you using to deal with this data?
- mattkrause 8y ago
  It's a hodgepodge. Mostly Matlab, Python, and R, with a few things that have tight time/memory requirements in C++. Matlab was really popular in neuroscience for a very long time, so we still have a lot of code in m-files, but most labs are moving towards Python (and a few towards R). The code quality varies a lot. Some of our "core" stuff is great, but there's also a lot of stuff that was written quickly and meant to be run once ("let's just try it"), which is cold comfort when you find it years later. People also come in with varying levels of programming skill. I'm going to try to do actual code reviews with the undergrads this summer to see if we can't make our stuff a little less embarrassing.
  - codekilla 8y ago
    I'm interested in using kdb+/q for applications dealing with large scale scientific data. Do you think there would be much interest in the neuroscience community? I'll add, I work in a lab and have seen the stuff of nightmares myself.
    - mattkrause 8y ago
      Possibly! I've not played with the time series databases much. Most of our queries would be something like "give me signals from channels A-C that are +/- 1 second from all events of type X." If it would help with something like that, it'd be great.
  - codekilla 8y ago
    I can't reply to your last comment...but yes..anything dealing with time series it's easily the best in the world. In a past life long ago I worked in finance and heard about it then. Recently, I was thinking about simulations and how to maybe deal with and analyze the timeseries data and I thought about it. Apparently, NASA is experimenting with it at their Ames Frontier Development Lab, astrophysics data is huge. Anyway, I am just kicking the tires at this point and trying to find ways to implement and play around with it a bit. If you or anyone you know develops an interest, feel free to drop me a line. grantroy _@_ caltech dot edu
loamseed 8y ago
Are there publicly available repositories of this kind of data? Where? What are the best modern methods to analyze them?
- mattkrause 8y ago
  Yup. The neuroimaging folks are really good about sharing data. If you want to look at MRI/MEG/EEG data, https://openneuro.org https://openneuro.org might be a good place to start. CRCNS (https://crcns.org/data-sets https://crcns.org/data-sets) has some neurophysiology data (i.e., from implanted or inserted electrodes). This sort of data is shared a little less often, in part because it's often acquired and stored in weird, homebrew formats, though that's slowly changing. ModelDB (https://senselab.med.yale.edu/ModelDB/ https://senselab.med.yale.edu/ModelDB/) has a large collection of computational models. These are mostly biophysical models, though there's some other stuff in there too. Depending on what you're looking for, there are other more specialized repositories. NDCT (https://data-archive.nimh.nih.gov/ndct https://data-archive.nimh.nih.gov/ndct) has mental-health related clinical trial data, though you'll have to do some paperwork if you want subject-level data, which is fairly common for clinical data. MIT has a collection of eye movement data sets: http://saliency.mit.edu/datasets.html http://saliency.mit.edu/datasets.html As for the best methods, this comment box is far too small to contain all my thoughts on that :-) It depends on your question and experiment. Sometimes, all you really need is a t-test (or the randomized version), but that requires getting the experiment just right. Other times, you might need a morass of signal processing and dimensionality reduction, fed into some giant multi-level Bayesian model in a vain attempt controls for all the stuff you neglected when you designed the damn experiment. Happy to send you some pointers if you have something specific in mind though! Physics has historically had a huge leg up on the other sciences because they had real models that made testable, quantitative predictions. We're finally starting to learn enough about the brain that we can do this for neural data too, and I'm really excited about that!
  - loamseed 8y ago
    Thank you for this wealth of information! I will definitely look through, and after digging around a bit I'd love to get some pointers. I have been curious about this for a long time, but uncertain where to look, so this is very exciting. Thank you again :)
mrep 8y ago
Ever read the book "On Intelligence" by Jeff Hawkins and if so, what are you thoughts on it? I've read that and ray kurzweil's books and On Intelligence was my favorite and got me super interested in AI but I haven't done much with it so I'm curious what someone who has a PhD in neuroscience thinks of it.
- beefcafe 8y ago
  Also “How to Build a Brain” by Chris Eliasmith. Interesting theories on how neurons represent functions and then build into a greater whole. I’m not a neuroscientist but my intuition says he might be getting closer to a solid mid-level (above the level of ion channels) understanding of brain function.
  - mattkrause 8y ago
    I haven't, though it's been on my list for a while. I'm collaborating with a woman who has also been working with him to model some of our data with NENGO, so I should really get around to that sooner rather than later.
jcims 8y ago
Just wanted to thank you for wading in here sand sharing your perspective.
- mattkrause 8y ago
  You're welcome! Random interesting stuff is what makes the internet worth keeping around, after all :-)
  - HNLurker2 8y ago
    Many neuroscientist turn to teaching mindfulness (e.g Sam Harris) because in their spiritual journey and academic neuroscientist journey they ask : Who am I? What do you think about this? Who is the I? Where can I find it? Is it just mysticism?