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."
Study and practice this in private. (Score:-1, Flamebait)
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.
To me, it looks like a situation very close to those amateur "computer programmers" who end up creating disasters using Ruby on Rails, JavaScript, PHP, MySQL and whatever the NoSQL flavor-of-the-day is today.
Nobody would have a problem if these "computer programmers" developed their software on their own, but then kept it to themselves. That is rarely the case, however. They release their software, without realizing the severe security holes it often contains. Then other people who don't know better end up using it.
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.
So unless you're aiming to become a professional in this field, rather than just an amateur or a hobbyist, I strongly recommend that you study this in private, for the sake of the community as a whole.
Re:Study and practice this in private. (Score:5, Insightful)
Re:Study and practice this in private. (Score:0, Interesting)
I'm hearing something else. It sounds to me like the professional does enjoy his job, he just doesn't like being dicked around by the shitty, half-assed, unnecessary attempts made by amateurs. A core tenet of professionalism is maintaining extremely high standards, which is something we just don't tend to see from non-professionals, regardless of the field. Being angered by bad work is a very good thing to see from professionals, and in fact it shows that they do care deeply about what they do.
Re:Study and practice this in private. (Score:1)
As the t-shirts and bumper stickers would have it: "People who think they know everything are annoying to those of us who do." :)
Re:Study and practice this in private. (Score:3, Insightful)
Re:Study and practice this in private. (Score:-1)
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.
Re:Study and practice this in private. (Score:2)
Re:Study and practice this in private. (Score:1)
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.
Re:Study and practice this in private. (Score:3)
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 successful, will this research advance the field?" to somebody like Larry Abbott, you aren't working on a 'real world problem.'
2) The state of the field is generally about 2 years ahead of the published literature. Unless you have collaborators who routinely attend talks and meetings, and know what people in your area(s) of interest are doing, it's very easy to wind up on the wrong track.
3) Modeling is only useful if it leads to experimental predictions that can be tested, and so needs to be part of an ongoing collaborative interaction between people collecting data, people analyzing it, and people modeling it. Without the entire loop in place, it's difficult to make useful contributions. Also related: outside of things like gene arrays, and a few other standardized approaches, most data in the field is collected by bespoke setups, so even understanding how to parse a data set requires interaction with the people who collected it.
So to answer the original questions:
(1) There are so many that it's impossible to specify. Very little computational neuroscience these days requires more than a workstation. You need to get into a collaboration to reduce the scope of the question for it to be answerable.
(2) It's probably easier than you think, but again it requires collaboration with somebody who's in industry or academia (the latter is probably easier). There are several people I know who informally collaborate doing neural modeling or data analysis with established labs. There are plenty of researchers who welcome informal collaboration, as long as it's competent.
(3) It really depends on who you wind up collaborating with, and the type of question. Neuron and Genesis are compartmental modelling simulators, which you'll only use if you wind up working with people on the molecular end of the spectrum (ie. figuring out intracellular processes). Most systems level work is done using Matlab (some Mathematica and Python as well).
(4) Get involved with non-DIYers. Find a lab to collaborate with! Go to SFN next year, and/or ICCNS/ICANNS/CoSyne/etc (see for example: http://www.frontiersin.org/events/Computational_Neuroscience [frontiersin.org]). Go to posters and talk with people. If you see something interesting, ask if they'd be interested in collaboration.. or ask them your question (1). It'll probably take multiple attempts to find the right group, but there are a ton of groups out there.
Finally, I'd just like to emphasize that working on 'real world' problems in neuroscience (computational or not) is a time consuming endeavor. If you don't think you'll be able to devote several hundred hours a year at the least, it'll be hard for you to find tractable problems.
Re:Study and practice this in private. (Score:1)
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.
Re:Study and practice this in private. (Score:-1)
I hope it's not like the other Coursera courses I tried to take earlier.
The material was generally okay. Sometimes the lecture videos were far longer than they needed to be, however, like the lecturer was just trying to hit the 10- or 20-minute mark on a given topic, even without having that much info to impart.
The quizzes were nearly useless, although the assignments were occasionally useful. Many courses do not have assignments, unfortunately.
The worst part, though, was the community aspect of it. The forums were absolutely useless. Half of the "discussion" involved people with Indian-looking names asking for the exact answers to quizzes or assignments, well before the due dates. The rest of the "discussion" involved people with Indian-looking names asking when they'd get their certificate of achievement, even when only part-way through the course. They completely drowned out any valuable discussion.
If Coursera offered some way to ignore all users that don't come from the USA, the UK, Europe, Canada, Australia, New Zealand and possibly Japan, then it'd be a useful learning tool. Until then, there's just way too much third-world crap to filter out.
Re:Study and practice this in private. (Score:0)
I found the Indian-sounding names very helpful when I was asking for answers to the quiz questions!
Re:Study and practice this in private. (Score:0)
Woo! Woo! Indians or curry Indians?
Re:Study and practice this in private. (Score:1)
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.
Mod the parent up, please. (Score:0)
It's disturbing, and somewhat sickening, to see that a well-reasoned and polite comment like that one is modded to -1.
I don't necessarily agree with what it says, but it's a lot better than so much of the other crap we see around here. It shouldn't be censored purely out of disagreement.
At the very least, the emphasis on the importance of professionalism and maintaining a high standard of care is something that /. readers should embrace, given how many of us are professionals. I hope that somebody does the right thing and mods it up, like it deserves.
Re:Study and practice this in private. (Score:3)
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.
Re: Study and practice this in private. (Score:0)
In every work place I've been, I've tried to encourage gifted or interested colleagues to contribute and improve their coding skills no matter what their background or experience. Of course, that also involves setting up workshops and a peer review process to ensure only quality code makes it into production. I've overwhelmingly found that we all become better programmers because of the exchanges and even debates this process encourages.
As a 20 year professional developer, I'd be tempted to fire you for your attitude alone.
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.
Re:Good start, now.... (Score:-1)
There's too great a risk, with self-taught people, for them to only expose themselves to the ideas that are appealing to them.
Spoken like a true academic. There are so many cases of non classically trained people providing significant scientific value and accomplishing incredible feats of human endeavor to warrant this statement clearly as protectionist. Academic snobbery is a scourge, but luckily most of the uneducated "hackers" don't listen to academic bores.
Re:Good start, now.... (Score:3)
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 submitter, computational neuroscience is still a nice field where somebody with some time, a willingness to learn, and a computer can make a contribution. Potentially a big contribution. Try to make contacts with academics. If you think you've got something, run it by the pros. As a masters student I shared an office with a neurosurgeon. I'd show him things I thought were awesome and he wouldn't think they were very useful. But a lot of the things I thought were trivial he'd get really excited about.
Re:Good start, now.... (Score:1)
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.
Re:Good start, now.... (Score:0)
Benjamin Franklin did most of his work outside of academia. .. and so on..
George Stephenson did most of his work outside of academia.
Thomas Edison did most of his work outside of academia.
Nikola Tesla did most of his work outside of academia.
Re:Good start, now.... (Score:0)
Yes create us a list, but don't include those on whose works have been extended from.
You my friend are looking for black ravens.
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.
Re:Feynman, and others (Score:0)
The vast majority of the "High school student discovers . . ." type news stories are actually stories of bright kids in academic lab internships who have the benefit of a well trained mentor and a good PR department. When you look closely at them most are work that probably was in significant part the mentor's idea. The extent of the student's input may vary, but is often far less than the lay press story would make you believe.
Re:Good start, now.... (Score:3)
Re:Good start, now.... (Score:0)
Good, but also be aware that the field of neuroscience itself is so large that even experts have to adopt a narrow focus. If you are up to the task, try to get experts from several different but related areas (both from within and from outside of neuroscience) to talk with and learn from each other (please)!
Re:Good start, now.... (Score:0)
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.
Well if academics cannot recognize that hanging around other academics and NOT exposing themselves to any other angle is exactly the same thing as only exposing themselves to ideas that appeal to them then the academic world is lost, and scientific method is being hampered.
It is like trying to prove that all ravens are black, by only looking for black ravens, instead you should be looking for pinks ones and save yourself time and effort.
I think all academics should stop trying to piss on the fires of the plebs, it just makes academia look like wankers.
Academics should be embracing all efforts big or small from all walks of life, and stop being so fucking separatist.
Re:Good start, now.... (Score:3)
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; although reality is usually shades of gray, this diversity of ideas all add insight.
The reason why academia may reject certain ideas is not because the ideas are radical (there are a myriad ideas in the university context), but because those ideas have not been thought through. There is no substitute for years of developing an idea and a coherent presentation of it.
I don't want to gild the lily here; there are many contexts which make it hard for someone to participate in academia, whether for economic reasons or time commitments. But that's a social reality. If many people don't have "a room of one's own" as Virginia Woolf put it, then we have to push for more than changes in the academies; we have to push for fundamental social reform.
Re:Good start, now.... (Score:2, Interesting)
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 something new-ish. He asks about access to data and how to communicate with 'the community.'
Unfortunately, 'the community' has terrible social/professional networking tools. Essentially, it all gets done in person at professional conferences, invited seminars, and during the critical grad school/postdoc. To some extent, professional communication only 'counts' if it appears in written form in one of a few credible journals. This is also part of academic elitism: there's no Neuro-Facebook where you have to wade through mountains of naive crap to find the nugget of real science. The OP wants Neuro-facebook, where he can talk about cool projects with other enthusiasts, and believes he'll be able to sort the good from the bad for himself. I think he's right. He may never find 'professional' neuroscientsts there, if it existed, but enthusiasts are definitely capable of doing novel, cool work.
Re:Good start, now.... (Score:2)
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).
Re:Good start, now.... (Score:2)
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 would be better. If you're that enthusiastic about it you could always subscribe to a few journals. Here's a few to look at:
http://www.springer.com/biomed/neuroscience/journal/10827 [springer.com]
http://www.frontiersin.org/computational_neuroscience [frontiersin.org]
http://www.cnsorg.org/journals [cnsorg.org]
That last one is a list of pertinent journals in the field.
The most important thing to get out of college *is* figuring out what you want to do with the rest of your life. If that's dropping out to start your own neuroscience company then by all means go forth and conquer!
Re:Good start, now.... (Score:-1)
Screw you all. Academics are worthless and so are the majority of the ivory tower proposal writers sucking on the state's teet for grant money. If you are at all competent you don't need academics, only the self directed drive to succeed. Yes it is very important to be 'professsional' but this does not require a degree. Many of the greats were dropouts or had no degree, how much easier is it now with the resources available today. No this is not an easy path or field but if you have the dedication the right people can succeed on their own. And yes please do know your stuff before attempting to contribute, this is more difficult to do than trying to be a Linux kernel developer.
Re:Good start, now.... (Score:-1)
Six Easy Steps to success:
1. Implant a computer interface in your brain.
2. A. Log all activity onto your workstation PC.
B. Log all sleep/wake times onto your PC.
C. Log all food intake onto your PC, along with times.
3. Review data logged and look for patterns.
4. Self publish the data.
5. Install Norton Antivirus for humans to remove malware.
6. Contact the NSA to compare your data with their information on you.
Re:Good start, now.... (Score:2)
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 spectrum of choices available to someone who wants to learn more about and do actual work in a subject. For example, there's plenty of online stuff available for virtually every subject. There are many forums where you can ask questions and discuss those subjects.
Your local library is a great resource for acquiring research. Via interlibrary loans, you can usually get access to any published research.
Often there are professional societies or non profits doing work in the area which accept laymen as members and participants.
Colleges and universities are a great source of knowledge, but you need not enroll (for the most part) for a degree in order to take advantage of them. Many seminars and lectures are open to the general public. You can selectively take courses in which you have an interest (sometimes, if you have good rapport with the department or the lecturer, you can just sit in on the class).
Or you can as Improv suggested, go for a degree. There are costs and risks involved which he regrettably ignores. You have to devote substantial time and money to getting a degree. And there is substantial risk that you don't complete the degree, especially, if you've never gone to college before and don't fully understand the expectations and distractions of college educations and lives.
Another problem is that some colleges and universities have a substantially anti-intellectual environment for religious, ideological, or hedonistic reasons. Do considerable research and shopping around before you settle on a college.
But having said that, you might find college to your liking. You might even find going on to advanced degrees to your liking as well. If you're on the fence as to whether you want to try for a degree, you might consider taking a course or two. Taking the occasional college course can help you decide. You might discover an aptitude or diligence that you didn't realize you had.
Re:Good start, now.... (Score:0)
What a douche.
Not gonna happen (Score:0)
Re:Not gonna happen (Score:2)
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 disregarding the educational gap, the field is the intersection of ML and medicine, two areas that are both extremely high-pressure and high-prestige. It will probably be the last discipline that breaks out of the cathedral and finds the bazaar.
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 actually put the time, perhaps you can end up writing a paper or presenting at a conference. Also, working with others is more fun (most times)
Re:Don't do it alone (Score:3)
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 volunteers.
I'd start looking at Itzekevich, and some of the more complex single neuron systems. (Partly because if you don't understand these, you're unlikely to understand a lot of the reasons why simpler neuron models with a lot more neurons are often a lot less useful than people want them to be.)
Have to run and teach a class, will try to get back to this later.
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.
Re:Difficult to do really well on a PC (Score:2)
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.
Re:Difficult to do really well on a PC (Score:2)
You can build a fairly large simulation that runs on modern inexpensive graphics hardware. Learning how to program those is where I'd start.
Re:Difficult to do really well on a PC (Score:0)
This sounds like the best advice. Also, if someone can conquer to some degree, the tough discipline of parallel programming (CUDA etc. ) on GPU's that's a good sign that their committed and capable of learning the art. I haven't but it sounds like a reasonable approach. I don't really buy into the formal education thing. I have a 4-year BSc. in Computer Science and know jackshit about it really. Formal education almost always distracts you from doing anything meaningful as there are too many tangents. you need to specialise and that means either research or sticking with one tangent (knowing the foundation of that tangent). Then if you can write a paper that is credited as being of a decent standard and something new and useful, you're effectively an asset to the area. At least that's how it should be in my book..
Prove yourself and you're ok..of course reality may be different and of course you always run the risk of duplicating someone else's work without knowing it, or creating something new that is of no real practical significance.
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 built by others, good programming/debugging skills should suffice. The network simulators are basically tools that allow you test out theoretical models of computing (which inevitably result from framing the question in the mathematical/physical language).
Playing with these models can be a lot of fun, but don't expect to find some fundamental principle if you don't know what you are looking for -- i.e. w/o a theory.
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.
Re:Data sets (Score:0)
... also Open Access Series of Imaging Studies (OASIS).
Re:Data sets (Score:0)
Also, what you are doing should be encouraged. Don't let these folks who are telling you to do your work "in private" or "leave it up to the pros" discourage you. They are misguided and their comments run orthogonal to initiative's such as edX, or (specific to your case) HarvardX's Neuroscience MOOC on Kickstarter. Just stay the course.
Re:Data sets (Score:0)
Well it is a bit ambitious to study different models of conciousness.
Just study one that works, get a nobel.
oh and before that get fifity million to spend on amazon cpu time.
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.
Re:how do you DIY neuroscience? (Score:1)
Absolutely false. You can easily recognize letters, numbers, faces, and I'm sure many other things (arbitrary) with a PC.
TGD Theory (Score:0)
The only viable of consciousness imho is at tgdtheory.fi
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.
Re:Some answers (Score:0)
...
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.
You might try starting with:
http://cratermatic.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). However, maybe there is a way to leverage from something like that.
Re:Some answers (Score:2)
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 (circles or basins), so this dataset makes a much better meal for analysis than something more complex like "is there a dog in this picture?".
Thank your son for me. The data is a unique blend of simplicity (of feature), volume (lots of data), and clear standard for comparison (the cratermatic output). Everything a researcher could want.
(*) The algorithm seems to have problems(**) with intersecting craters, craters contained within craters, and partial craters (incomplete arc of lip visible). It detects many "tiny" irregular craters which seem at first glance to not be actual craters.
(**) This is against a background of many craters correctly recognized. The program is quite useful, I'm just wondering if there is a better technique
Systematicity, and Fodor & Pylyshyn (Score:5, Informative)
Re:Systematicity, and Fodor & Pylyshyn (Score:2)
Re:Systematicity, and Fodor & Pylyshyn (Score:1)
Re:Systematicity, and Fodor & Pylyshyn (Score:2)
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 framework that has been vetted by lots of other researchers.
2) Access to latest data and tools: Matlab costs quite a bit (esp. with all the toolboxes that you might require). Most universities give you the license for free.
3) Like minded individuals are (for better or worse) almost all at universities and research labs and the main interactions come from conferences. Journals are good for non-interactive peer review, but if you want collaborators, you need to head to conferences. This is also where the university name (and financial backing) can help - "Oh, you work with $BigName? I'd love to collaborate with you!"
You don't have to spend a lot of money either. You can take non-degree enrollment (so you can work at your own pace) while still having a lot of access to the tools, data, and collaborators. In addition, you haven't mentioned your background. So you might find it harder or make trivial mistakes that betray your inexperience or out-of-field characteristics. 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 have known lots of people in industry who swear by Matlab (for example), while not realizing how poor it is compared to more sophisticated optimization tools, especially when you get into large data-sets (which I assume you will be involved with).
Re:Subscription to resources (Score:1)
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 knowledge. On a related note, I had a neuroscience professor who would say that if you're too focused on the tools you're using, you're stepping away from being a researcher, and towards being a technician.
O'Reilly wikibook Computational Neuroscience (Score:1)
Re:O'Reilly wikibook Computational Neuroscience (Score:1)
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).
There's plenty of free software available (Score:0)
I have a PhD in a field related to computational neuroscience as well. I don't want to get into the finer points of academic research, but I do want to point out that everything I did as a grad student was done using open-source software. It's true that without formal training, you'll never get anything published, but it sounds like you're just looking to apply some of the principles to other fields. Given that that's the case, I say, go nuts. All the tools you need are free to use. Consider GNU Octave instead of Matlab. Pick up Python, SciPy, and NumPy. Check out open-source projects for data-mining, like Gephi. Get in touch with professors that are producing data you'd like to look at. If they've already published it, they're probably willing to share it. If they haven't published it yet, they'll probably just ignore you.
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 on?
The first step would be to get a very solid theoretical grounding in your field. "The basic concepts behind Computational Neuroscience" is a start, but getting a good grounding in at least neuroscience and cognitive science, as well as other subdomains of psych, would help you tremendously (that's a soft way of saying that it's probably imperative to have this background). In the process of doing so, you will become familiar with research that has been done, and you will get a better idea of which specialized topics in your field appeal to you; furthermore, you will have an idea of the research that's currently being done, and which research it's reasonable for you to pursue with the specific computational and data resources that you either have at your disposal or with resources to which you can gain access.
I realize that there's a strong DIY/autodidact ethos here, but for the purposes of getting a theoretical grounding, consider enrolling in a grad program, if it's at all feasible. This approach is likely to bring you up to speed more quickly than learning things on your own (consider exploring a city which you've never visited: you can figure things out on your own, but a qualified guide or resident can show you the most important sights while wasting a minimum amount of time).
Google Scholar will be a very useful resource for you, but if you can, try to get access to a university library so that you can electronically access the journals to which they're subscribed - I understand that some very good libraries provide paid access.
(2) Is it easy for a non-academic to get the required data?
There's too many variables to provide an answer to this question. Sometimes, you get lucky and obtain data with a minimum of fuss. Sometimes, you can't obtain data at all because whatever person or organization has that data doesn't want to share with you. Sometimes, the data you may want has not even been generated yet (in which case, it's useful to have a lab, get grants, etc.).
There are some data repositories which will not give you access without some sort of qualification, such as being an NIH-funded researcher. To obtain data from these, you may want to get affiliated with a lab at a university, and ask them to obtain data for you - if you manage to convince them that you'll be a valuable asset, they may be willing to do you the favor of obtaining data from the repository, as well as sharing their own data.
Sometimes, you can get data by contacting labs directly. Needless to say, being affiliated with a university in some capacity (i.e. perhaps being a research assistant somewhere, or at least having a .edu email address) will increase your chances of obtaining data. Unfortunately, some labs will invariably not want to share their data at all.
(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?
Identify the research areas that interest you. Look up papers in those areas on google Scholar (plenty of full texts are available in PDF and HTML). In the methods sections, it is conventional to state which programs the researchers used.
Speaking from personal experience, Python, Matlab, and R have served me well through providing a number of useful modules and functionalities, including neural net libraries, text processing, and statistical analysis. Don't underestimate what you can do with these basic tools and some open source librar
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 through a lot of straw to find the gold. One of the advantages of graduate school is that there are experts who can help you with this.
Re:CMU computational neuroscience course (Score:2)
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
Nonsensical (Score:0)
What a nonsensical question.
OpenWorm (Score:0)
If you try to tackle an interesting problem by yourself from scratch, you will most likely reinvent the wheel and produce nothing of value. This is, in my opinion, what I see at Computational Neuroscience meetings where people (especially new grad students coming from other fields) model the same things, in the same way, over and over again, hoping to be original but failing. Why don't you try to join an established team that welcomes outsiders (who know how to code). OpenWorm is a great example. http://www.openworm.org
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.
Re:You may get some milage out of this software (Score:1)
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 most people is hindered by an illusion of completeness. The components and expression that lead to an illusion of completeness are a study in artistic analysis themselves. A less able person will unfold and express an action over a considerable span of time. Understanding the sequence, organization and simplifications of the less able action is a fascinating exersize.
My study in information theory and activity in ham radio has led me to focus on quadrature phase demodulation as a candidate for some kinds of neural function.
My study of embryology at St. John's suggests to me that there is an initial layout of the nervous system.
From topology and paper folding I have a feeling that folding and surface tension processes are involved with what later appears as a tiled surface of processing layers wrapped around an inner device that stores motor motion plans.
Suppose one explores the hypothesis that nerves start out a lot alike. Clusters appear around neurons due to a diffusion limitation. What else about the layout of the brain can we determine from embryology?
Extracting information from nerve pulses is done in layers. If the layers weren't connected during the cellular development phase, how do the layers later pass information around? Are the folds of the brain part of this?
How does the recently reported glial cell expansion and contraction fit into the connection scheme? Eventually the abstractions are reorganized into motor impulses that move muscles.
Look at just one bar of a Mozart piano concerto. The pianist moves his fingers eight or nine times. It is interesting how ideas have an association with movements.
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 of the resources that professionals have access to -- hardware, proprietary software, and good datasets, and most of all, time. If you try to follow the cutting edge, you will find that it is advancing more quickly than you can possibly keep up with (if this is not a full time job). Don't get bogged down in the details of a project, and don't worry if your work is necessarily realistic. Try to stick with more theoretical issues, where you can complete a project in a reasonable span a time and might possibly develop a line of investigation that has been ignored by the professional community.
Read the high-profile, general interest journals (Science, Nature). That's where you will find the novel, simplistic (and hopefully, groundbreaking) models. You may also find announcements of high quality datasets. The more specialized journals tend to have publications focusing on optimizing one detail or another. These are essential for science, but there is no way that an amateur will be able to keep up with this or implement models that are this detailed.
If you're not careful, you'll spend most of your time struggling with the idiosyncratic formatting of various datasets.
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 grasshoppers to verify the computational model described in this wonderful study in the journal Science.
Elementary Computation of Object Approach by a Wide-Field Visual Neuron
Nicholas Hatsopoulos; Fabrizio Gabbiani; Gilles Laurent
Science, Vol. 270, No. 5238. (Nov. 10, 1995), pp. 1000-1003.
This studied shows that a single neuron can carry out a multiplication of two independent input signals, and the student was able to reproduce these results and began to improve the model.
If you take the MCB80x MOOC currently being offered for free over at HarvardX, you can see videos on how to collect these data.
Disclaimer: I am the co-founder of the company that makes the SpikerBox (backyardbrains.com). We started it to help nurture a community of DIY Neuroscientists.
Topographica (Score:1)
Pity it was posted anonymously (Score:2)
Re:Pity it was posted anonymously (Score:1)
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
Re:Pity it was posted anonymously (Score:1)
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