Ask Slashdot: DIY Computational Neuroscience? 90
An anonymous reader writes "Over the last couple years, I have taught myself the basic concepts behind Computational Neuroscience, mainly from the book by Abbott and Dayan. I am not currently affiliated with any academic Neuroscience program. I would like to take a DIY approach and work on some real world problems of Computational Neuroscience. My questions: (1) What are some interesting computational neuroscience simulation problems that an individual with a workstation class PC can work on? (2) Is it easy for a non-academic to get the required data? (3) I am familiar with (but not used extensively) simulators like Neuron, Genesis etc. Other than these and Matlab, what other software should I get? (4) Where online or offline, can I network with other DIY Computational Neuroscience enthusiasts? My own interest is in simulation of Epileptogenic neural networks, music cognition networks, and perhaps a bit more ambitiously, to create a simulation on which the various Models of Consciousness can be comparatively tested."
Re:Study and practice this in private. (Score:5, Insightful)
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As the t-shirts and bumper stickers would have it: "People who think they know everything are annoying to those of us who do." :)
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Re:Study and practice this in private. (Score:4, Insightful)
I would suggest professionals decide not to use the code from well-meaning but undertrained amateurs then. I mean the only way it would impact them is if the code is taken up and used. Outside of that, they are getting paid to do something specific and doing what you are hired to do was as far as I know, a hallmark of professionalism.
Re:Study and practice this in private. (Score:4, Insightful)
Ignore the naysayers. Do what you love. As for programming, professionals have created more security nightmares than amateurs.
Model of Consciousness seems a bit ambitious. Something easy to measure and readily available is how to hit a baseball. A fastball is moving faster than your eyes can track it, so you have to create an internal model of where it's going and swing a mechanical system (your arm and the bat) at the right time and place to knock it into the stands. It would be interesting to determine what inputs the brain uses and model the control system.
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Re:Study and practice this in private. (Score:4, Interesting)
It's very noble to want to learn and to further educate yourself. But for the sake of the professionals in the field, I do encourage you to engage in your study and practice of this field in private.
I have a better idea. Completely ignore the above, terrible advise. While there is considerable value in doing your own work privately for a time, you need to communicate with others, if you want to improve your game. That means not just dumping code or whatever on the internet, but actually reading and listening to who else is already doing this sort of stuff. Keep in mind that most of your communication should be input - learning from others.
Months or years later, a disaster of some sort happens (a security breach, data loss, and so on), and a professional gets dragged in to try to solve the problems. This wastes the professional's time, which is often very expensive. It also angers them, because it's a problem that would have been unavoidable had the amateurs just kept to themselves.
Sounds like the "pro's" time isn't being wasted, if he's getting paid. And if you or anyone actually are "angry" over something this trivial, maybe you ought to find a different line of work.
So unless you're aiming to become a professional in this field, rather than just an amateur or a hobbyist
The only difference between a professional and an amateur/hobbyist is that the professional gets paid and tends to be a bit more knowledgeable. And that's the source of this friction between professional and amateur. The amateur is doing some of the professional's work for far cheaper. It's screwing with the professional's business model Keep that in mind when you read of professionals complaining about the amateurs.
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To amplify the above comment, as a neuroscientist with a computational background: don't try to go it alone.
There are a few reasons for this:
1) Research in the field is done by groups because the main problem in generating an 'interesting simulation problem' is carefully defining a scope and a target. That's really hard to do, and generally involves careful discussions between people with different knowledge bases and priorities. If you can't give a clear and succinct answer to the question "How, if success
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Re:Study and practice this in private. (Score:4, Interesting)
Not affiliated at all with Coursera, but I noticed this free course [coursera.org] the other day. Starts in January.
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The HarvardX equivalent has already started with "Fundamentals of Neuroscience" [mcb80x.org] It is a basic neuroscience course with a twist, they succeeded funding a Kickstarter [kickstarter.com] campaign in collaboration with BackyardBrains to supply a hundred spiker boxes to enable a citizen-science approach to neuroscience
The presentation is also way better than what I have experienced on Coursera so far.
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So where was that 'pro' when it was time to analyse the software for suitability? Either not there or too busy being all angsty and professional.
Check out NEST (Score:4, Informative)
Good start, now.... (Score:3, Insightful)
Good start, now go do some formal study and get a degree. There's too great a risk, with self-taught people, for them to only expose themselves to the ideas that are appealing to them. Academic fields recognise this; you're not going to be ready to contribute to the cutting edge unless you put your ideas in the field up for reshaping by people who know more than you do, and that's a good thing.
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And a lot more who fail miserably. Sometimes taking down other people with them.
It's certainly possible to make valuable contributions without a formal education, but it's not the norm. If you look at the people who do it, they're almost all brilliant; humble, at least about their work; extensively, and widely, informally educated; and benefit from a lot of interaction with people who DO know something about the field.
Not having those things, at least to some degree, is the recipe for a crackpot.
To the su
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So how about some examples, then? There are "so many cases", after all, so you should have no problem giving us 10 or 20 of the most convincing examples.
It would be more convincing if you limited them to contributions to well-established fields, as well. There's nothing impressive about basic discoveries made in the infancy of a new field of study, when EVERYBODY involved is essentially an amateur.
Feynman, and others (Score:3)
So how about some examples, then? There are "so many cases", after all, so you should have no problem giving us 10 or 20 of the most convincing examples.
It would be more convincing if you limited them to contributions to well-established fields, as well. There's nothing impressive about basic discoveries made in the infancy of a new field of study, when EVERYBODY involved is essentially an amateur.
Richard Feynman is one of the few people to have decoded/translated a Mayan heiroglyphic codex [lds.net].
He did this as an amateur without anything close to a related degree.
This kid [foxnews.com] discovered a new dinosaur,
Just google "high school student makes scientific discovery" or "college student makes scientific discovery" for a big list.
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You'll find that the academic system is not as elitist as you would think, at least not in countries where the barriers to entry are low. It only takes a few years to work through a basic degree, and if you have any ability, you can soon move beyond that to real research and contributions.
The reality in academia is that you can put up any ideas at all, and the peer-reviewed journals have a huge diversity of contributors. Sometimes the strongest papers are those that argue an idea to its logical extreme; alt
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You'll find that the academic system is not as elitist as you would think
This, in a thread that was started by some academic saying, "There is no way to do interesting computational neuroscience unless you quit your job, convince an admissions board you're worthy of their Institution, and spend 5-6 years as a minimum-wage apprentice/grad student." The GGP is basically saying he won't even talk to people unless they've been properly credentialed, and is absolutely being an elitist snob. The OP doesn't seem to want to do publishable original research, but at least to do somethin
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I have been in academia for more than twenty years and can say without a doubt that being around experts in a field cannot be replaced.
What happens if you want to do something interesting in the field and can't afford to chill with experts for twenty years?
College can be life-changing. It can also be a very bad choice. A lot of people have dropped out with high debt and a weak, partial education. I know because I've done both (well, no high debt, but I have used up a lot of years that I could have been doing something else).
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I have been in academia for more than twenty years and can say without a doubt that being around experts in a field cannot be replaced.
What happens if you want to do something interesting in the field and can't afford to chill with experts for twenty years?
Then computational neuroscience is not going to be your bag. I also learned in this time that coming to college doesn't always mean graduating with a degree in order to find out what you want to do with the rest of your life. But, if you want to do scientific research and have any impact then you should go the research and academia route. If you want to just play, go play. You don't have to be an enrolled student to go to the library and read journals, although a lot of them are no longer printed so sooner
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Good start, now go do some formal study and get a degree.
Credentialism rears its ugly head. College is not automatically the best choice.
There's too great a risk, with self-taught people, for them to only expose themselves to the ideas that are appealing to them.
Risk and great reward. After all, if you expose yourself only to ideas you're interested in, then you learn them because you are interested. I think enthusiasm is more important than variety. College can expose you to ideas outside of your experience which you can be enthusiastic with. But it also exposes you (with a considerable time and financial commitment) to stuff that won't be so interesting or relevant.
There's a whole
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On the plus side, Dayan and Abbott is actually a graduate-level text, but you're utterly right. To keep up with just the unsupervised learning methods that it covers requires at least a stats course, a linear algebra course, and a couple of calculus courses. To make things worse the newest edition is 8 years old, which is a significant portion of the lifetime of the modern cogsci field.
You might appreciate this [wordpress.com] blog, which is at least about availability and help might get you more well-read, but even disreg
Don't do it alone (Score:1)
Best chance of success is to approach a lab and volunteer your time. You will get a chance at good discussions and their expertise and insight, and they will get your time and effort. They probably have nice little problems like what you want, but they are not just going to write them up for you because 1) takes too much time and they rather write papers and grant applications; 2) a student in their lab can do it. If it really all it needs is a pc, then they have people to do it. If you do a good job and a
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I gotta agree with this.
You can do a lot on your own, and I personally don't see any reason why not to (unlikely a poster above, hey, if you write shitty code, I just won't use it - and it's not like there aren't plenty of neuroscientists writing shitty code because being a theorist doesn't make you a programmer.) But getting more exposure to other people's ideas and pointers to resources is going to do you a world of good. And you don't need much of a background if you have computer skills - people love vo
a few easy steps (Score:1)
1. run the protein folder as the background task on your desktop
2. this guy shot himself in the head and cured his neurological condition. calculate possible trajectories.
3. ???????
4. NOBEL PRIZE
Difficult to do really well on a PC (Score:2)
Yes, there's open-source neural simulators for the PC out there, but the leading edge of neuronic simulation is doing it in hardware, which is thousands of times faster than modelling it in software.
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can you give examples? perhaps you mean that implementing brain-inspired special-function processors is best done in hardware - if you want a widget that detects pictures of cats or something. study/understanding is not often rate/scale-limited.
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You can build a fairly large simulation that runs on modern inexpensive graphics hardware. Learning how to program those is where I'd start.
Computational neuro is more than simulations (Score:2, Interesting)
The point of running all the simulations is to aid in the understanding of how neural circuits compute; they aren't all that useful outside a theoretical framework. Computational neuroscience heavily uses concepts from dynamical systems, statistical inference, information theory etc. If you want to figure out new ideas about how neural circuits compute or represent information, then some exposure to these topics is essential. On the other hand, if you simply want to play around with and/or tweak models buil
Re:Computational neuro is more than simulations (Score:5, Interesting)
+1
That all being said, going out and playing with some of the established tools, and reimplementing some classic models (or building models off of wet lab papers, or whatever) is going to build you up a great skill set, and make it a lot easier to find a lab position if you want to go that directon (either a paid one or a volunteer one, each has advantages).
I'm personally enough of a biologist to feel compelled to point out that a lot of what has been done in larger networks has diverged from biology in critical ways - some of it might be interesting in its own right, but it's not really neuroscience in any meaningful way.
Get a solid grounding in Neuroscience. (Kandell, Jessel and Schwartz, Principles of Neuronal Science, is the standard text, it's excellent, and highly torrented.) Please, please, please take some time to understand the variety and complexity of single neurons - they are way more complicated than many of the people who model systems with high numbers of neurons let on. Having a system with 100 billion simulated neurons means an awful lot less if the neurons themselves are shit.
Re-implement some classic systems from scratch. Yeah, I mean start with Hodgkin and Huxley, and build up from there. You will learn things from doing that yourself that you'll miss by just diving in with established tools. (And a lot of the established tools have issues.) Itzikevitch, Wilson, and Trautenburg are all favorites of mine off the top of my head. Strogatz is great as an introduction to dynamical systems.
Data sets (Score:1)
Look into 1000 Functional Connectomes and Human Connectome Project. These are two (perhaps 3 or even 4 since HCP is ambiguous) open access, neuroimaging, data sets. Python is a great way to go. Equip yourself with python + numpy/scipy/matplotlib. You will do great.
Whatever you do (Score:2)
Focus on the 'language' mystery.
'Solve' that and you will have solved consciousness and a
few things that come serendipitously with it (like 'music cognition').
how do you DIY neuroscience? (Score:1)
you need a hundred million $$$ of supercomputing computer power to run any useful computations. i guess you can rent some computing power on amazon, but that is going to cost you.
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Absolutely false. You can easily recognize letters, numbers, faces, and I'm sure many other things (arbitrary) with a PC.
Why ask Slashdot? (Score:1)
Seriously, if you're asking this bunch of bozos for advice, you're already off-track.
The right way to learn about cognitive neuroscience is to go where people are DOING it, not where people talk out of their asses about it!
Getting better access to information (Score:4, Interesting)
I am not familiar with this particular academic community, but generally it is not easy for an academic to get data. The most useful resource is probably the co-operation of those who have gathered the data, and in order to get that you have to find out who they are. The inclination to be helpful varies immensely across disciplines and people within disciplines, but all you lose by trying to make contact is possible embarassment. Step 2 in the list below will give you a tag to use when introducing yourself, which may make you feel less awkward and therefore may improve co-operation.
I suggest 3 steps, in increasing cost, that are likely to help:
Some answers (Score:4, Informative)
I research hard AI. In my view thinking through and tackling example problems is the best way to explore a topic. If you require your system to mirror our current understanding of neuroscience, then you're essentially researching the algorithms of the brain.
If you're specifically looking into epilepsy and related, consider checking out William Calvin's [williamcalvin.com] website. He's an experimental neuroscientist [wikipedia.org] from University of Washington, who wrote many books that explain the neurological foundations of the brain in readable form with good detail.
(1) What are some interesting computational neuroscience simulation problems
Pretty much anything AI falls under that category. Go over to Kaggle.com [slashdot.org] and check out some of their competitions, including their past competitions. Check out the Google AI lab [google.com] and see what they're doing, and check out recent publications [arxiv.org] to see what people are trying to solve. Ask yourself: Are humans better than the computer, and can it be done better?
Here's a video [youtube.com] of a system that uses neuron simulation (of a sort) to recognize hand-written digits. A hand-written digits dataset is in the UCI archive below.
(2) Is it easy for a non-academic to get the required data?
Generally, yes. UCI has a repository [uci.edu] of machine-learning datasets. The researchers supporting Kaggle [slashdot.org] competitions frequently release their data.
I've found that researchers are generally approachable, and will give away copies of their data (I have 4 datasets from researchers). As a personal anecdote, last week a researcher from this very forum sent me his dataset of Mars altitude images [wikipedia.org] - I'm trying to come up with an algorithm to recognize craters.
(3) I am familiar with (but not used extensively) simulators like Neuron, Genesis etc. Other than these and Matlab, what other software should I get?
In my view, pick a computer language that has a wide support network of libraries, and code things from scratch.Something like Perl or R. At some point you will want to break open the box and see what's actually happening inside, and familiarity with the system (having constructed it) is key. You will want to insert trace statements, print out intermediate results, and so on. Most of the pre-built systems don't have what you will ultimately want, and building simulation objects isn't terribly hard.
(4) Where online or offline, can I network with other DIY Computational Neuroscience enthusiasts?
Please let me know if you find any (by posting a response).
I've found that most AI enthusiasts are really "big data" enthusiasts, and most of them are all about business rather than AI. The IRC AI chatrooms [irc] are all but dead, and most of what is there are students asking for help with their homework. (Although to be fair, the lurkers there know everything about AI and can answer questions and make suggestions if you're stuck.)
The NEAI meetup [meetup.com] in Cambridge is mostly spectators - people who want to find out about AI or how to use AI ("how can I use AI to improve the performance of my financial company?"). I hear there's an AI meetup out on the West coast that's pretty good.
See if there's a meetup [meetup.com] in your area for something related, or start one and see if anyone shows up.
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You might try starting with: http://cratermatic.sourceforge.net/ [sourceforge.net] (disclaimer... this was written by my son some years ago as a NASA intern). It is nothing like AI, but is a classical topography algorithm (basin filling).
Your son is the one who sent me the data :-)
The online project does not have the dataset, and the dataset links on that page are broken. After talking with your son a bit he agreed to send me the data.
Looking at the cratermatic results, I notice that the algorithm has problems with certain situations, mistakes that a human analyst wouldn't make(*). This is what piqued my interest in the project - what is it about the human algorithm that does so much better?
The recognition feature is relatively simple (circ
Systematicity, and Fodor & Pylyshyn (Score:5, Informative)
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Re: Your (excellent) questions. (Score:1)
Subscription to resources (Score:2)
Kudos on your dedication to be self taught, but the questions you raised are one of the things that a university is great for. To make a meaningful contribution in mathematically-oriented fields (such as computational neuroscience), you need to have the following:
1) Access to latest journals and papers: This should help answer question (1), (2), and (3) - use the tools others are using. If you find an open-source tool, that is great. But often, people in the field will expect you to use a standard framewor
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Most graduate (including Ph.D.) students take a lot of classes on basics (at the start) so that they know the vocabulary and concepts necessary to read and understand the cutting edge research. Without that, you are likely too dependent on the tool.
I'd modify that to saying that without a sufficient theoretical background, even if you have access to the best software and hardware tools, you will not be able to do very much with them that will be of interest to anyone. Lots of great research (including research in the domain of computational neuroscience) is done on FOSS tools that can be downloaded in a matter of a few minutes; the prerequisite is having sufficient knowledge in a particular domain. Conversely, no software will compensate for a lack of
O'Reilly wikibook Computational Neuroscience (Score:1)
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I took a course with Michael Frank as an undergraduate, we used this book. It is one of the best textbooks I've ever encountered.
Also, as someone else mentions below, emergent (http://grey.colorado.edu/emergent/index.php/Main_Page) is a cross-platform open-source simulator that goes with the book (most of the examples are in PDP++ which is a prior version of emergent).
I'll try to provide some input (Score:1)
Note: I've published cognitive and neuroscience research that utilized neural nets. I'm not specifically that knowledgeable in the specialized topics listed after point (4), but perhaps I can provide some useful general information about how to go about acquiring resources that may help the author, and perhaps others, increase their chances of success in their research efforts.
(1) What are some interesting computational neuroscience simulation problems that an individual with a workstation class PC can work
Computation Neurosci summer course (Score:1)
I have a PhD in neuroscience.
If you can afford it, apply to take this course: http://hermes.mbl.edu/education/courses/special_topics/mcn.html [mbl.edu]
It is taught by some of the best in the field, and many alum have gone on to do good work.
CMU computational neuroscience course (Score:2)
I teach a graduate course in computational neuroscience at CMU. My lecture notes, exercises, and Matlab software are all available online via my home page, at http://www.cs.cmu.edu/~dst [cmu.edu]
I disagree with the notion that only professionals should speak publicly about their scientific work. Amateurs should be welcome in any branch of science. Who knows where the next contribution will come from? And there is plenty of disappointing work from tenured professionals. So read the journals, but be prepared to wade th
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Dave's course is certain to be interesting.
A good way to get up to speed in research IMHO is to look at the problems in publications, and try to reproduce the indicated work. You can do this with fairly old (5-6 years) publications too, usually at that point data sets etc are available.
Edmund
You may get some milage out of this software (Score:2)
Emergent Neural network simulator. [colorado.edu] If nothing else it will give you a good baseline of how far you can push the envelope with a single workstation.
Just installed it on my machine and it looks well crafted and quite versatile.
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One non-academic job with interesting values. (Score:2)
I work with severely disabled children as a classroom aide. While the job is an aide position, I am also privileged to have weeks and even months of exposure to a specific disabled person.
If your computer career is declining due to age, education or platform problems I mention this kind of employment as a kind of work that may provide you with many observations that may provide problems or ideas regarding computational neuroscience.
It is my opinion that observation of motor actions and neural operation in m
join/start a club for amateurs (Score:1)
As a professional evolutionary biologist, my advice is to approach this as a hobby and don't pretend that it is anything else. Look for other hobbyists and discuss your projects with them (while learning whatever you can from professionals), and just focus on whatever you find interesting. Do not try to compete with the professionals; you probably don't even want to copy them, except when they have introduced a novel approach to a problem that can be taken in many directions.
You will not have access to many
Collect your own neural data! (Score:1)
I have a PhD in neuroscience, and teach at the Methods of Computational Neuroscience course in Woods Hole that patluri recommends in another comment. We begin the course by having each student collect their own data using the SpikerBox, and their mobile phones. These data can then be analyzed in Matlab, Pyton, etc. The experiments you can do are slightly different then what you are after, but it may be a good starting point. This summer, for example, one student collect data on the visual system of gras
Topographica (Score:1)
Pity it was posted anonymously (Score:2)
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I'm a neuroscience doctoral student studying epileptogenic networks. I would have messaged you if I could.
You can now. Had to post AC since I had trouble logging in earlier. Look fwd to hearing from you. Thx
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I'm a neuroscience doctoral student studying epileptogenic networks. I would have messaged you if I could.
Sorry for the double reply. Realized you didnt know where to messag/emaile me at. Now you do :) I guess this is what all the go-academic posts are talking about- the ability to network easily :) Thx