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Ask Slashdot: DIY Computational Neuroscience?
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."
It's open source, and integrates with Python and the whole SciPy suite. I'm not a neuroscientist, but I work in one's lab. I haven't used the software extensively, but it's installed on a Linux VM wasiting for some love while we work on other things. http://www.nest-initiative.org/
Those people who think they know everything are a great annoyance to those of us who do. (Isaac Asimov)
It sounds like the "professional" is sick of his job.
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.
For every problem, there is at least one solution that is simple, neat, and wrong.
You can't learn such things by reading "a book". An undergrad devoted to it would be entry-level at best. You're going to need many, many books. And you're going to need an extensive mathematics background, among other knowledge. College is often a waste, but this is not one of those times. Honestly, this strikes me as one of those of those attempts by people to throw out some intelligent-sounding buzzwords without actually putting forth the required effort to learn about it.
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.
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)
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
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.
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.
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.
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.
Not affiliated at all with Coursera, but I noticed this free course the other day. Starts in January.
All my liberal friends think I'm a conservative, all my conservative friends think I'm a liberal.
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.
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.
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').
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.
The only viable of consciousness imho is at tgdtheory.fi
As the t-shirts and bumper stickers would have it: "People who think they know everything are annoying to those of us who do." :)
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!
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:
Mike O'Donnell http://people.cs.uchicago.edu/~odonnell/
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 website. He's an experimental neuroscientist 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 and check out some of their competitions, including their past competitions. Check out the Google AI lab and see what they're doing, and check out recent publications 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 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 of machine-learning datasets. The researchers supporting Kaggle 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 - 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 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 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 in your area for something related, or start one and see if anyone shows up.
I have a recent PhD in neural computation, though from a functional cognitive and language modeling perspective, and not a neuroanotomical modeling perspective -- so it may be a different area than you're interested in. From a high-level perspective, neural computation has moved a lot in terms of scale in the past two decades (simulations can have millions of nodes), and it has moved a lot in terms of modeling the processes of individual neurons and neurochemistry. Very high-level functional mapping work has also moved a good deal with fMRI, EEG, and MEG becoming relatively inexpensive and very common techniques in cognitive experiments. One area that, in my opinion, has moved very little in the past 20 years is the ability for neural networks to learn non-trivial domain-general representations and processes, and to generalize from those representations and processes to novel (untrained) instances. In the late 80s, after connectionism had made return with Rummelhart and McClelland's popularization of the backpropagation algorithm and demonstration of its utility in a number of tasks earlier in the decade, a good deal of the literature demonstrated very basic limitations and failures of these systems to generalize to untrained instances, or to move away from toy problems. Fodor and Pylyshyn's "Connectionism and Cognitive Architecture" is a classic paper from that era, and Pinker wrote a lot language-specific criticisms as well. Stefan Frank has the most recent long-standing research program in this area that I'm aware of, and his earlier papers have good literature reviews that can further help guide ones background reading. There have been some limited demonstrations of systematicity with different architectures (like echo state networks), and comparatively little work on storing representations and processes simultaneously in a network, but so far these are long-standing and fundamental issues that need revitalization. When convincing demonstrations do arise, they'll likely not need more than a desktop to run, as it will be demonstrations in learning algorithms and architectures, not scale. For non-neural folks, classical neural network architectures are essentially very good at pattern matching and classification (e.g. being trained on handwriting, and trying to classify each letter as one of a set of known letters (A-Z) that it's seen many hundreds of instances of before), or things that involve a large set of specific rules (if X then Y). They're much less good at things that involve domain-general computation, that involve learning both representations and processes and storing them in the same system (i.e. let's read this paragraph and summarize it, or answer a question, or let's write a sentence describing a simple scene that I'm seeing). That's not to say that you couldn't make a neural system that did this -- you could sit down and hard-code an architecture that looked something like a von-neumann CPU architecture and program it to play chess or be a word processor, if you really wanted, but the idea is developing a learning algorithm that, by virtue of exposure to the world, will craft the architecture as such. The idea being that, after years of exposure, the world will progressively "program" the computational/representational substrate that is the brain to recognize objects, concepts, words, put them together into simple event representations, and do simple reasoning with them, much like an infant. I hope that helps. Of course all of this is written by someone interested in developmental knowledge representation and language processing, so it may be a completely different question than you'd wanted answered. best wishes.
I found the Indian-sounding names very helpful when I was asking for answers to the quiz questions!
My questions: (1) What are some interesting computational neuroscience simulation problems that an individual with a workstation class PC can work on? ** These come up more frequently than you might think. Even what you'd think of as a regular home or office PC can do a lot with 8-16 gigs of memory, let alone amounts beyond that. I'd suggest that you start looking at http://www.kaggle.com/ as a place to start. Also, start looking at the discussion groups that you can find on (I hate it, but use it) LinkedIn. I prefer the discussion groups that you can get at the American Statistical Association, and even the listserve discussion groups for various statistical software packages (e.g., R, Stata, SAS). (2) Is it easy for a non-academic to get the required data? ** It depends on the problem being examined, and who "owns" the data. For Kaggle competitions, the data is given to you. For other projects, a lot of data is becoming "open sourced" so that people can get to it publicly. So, that's a qualified yes for some things, and a no for others. (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? ** I tend to lean on Stata and R. Will be moving over to R after finishing current research project. It depends on the areas you want to examine. If you're willing to deal with the "learning curve" for R, I'd go with that. It's free and has a fantastic community. (4) Where online or offline, can I network with other DIY Computational Neuroscience enthusiasts? ** I hate LinkedIn, but I use it in my own field. You might try that, as well as G+ initially. I'd also be looking at the American Statistical Association and related professional groups. The listserves for various statistical software packages are good, but they get nasty about off topic posts (tangential to the use of the software) ** I think that the related StackOverflow forums would be very good. I've had good results with them.
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).
Randall O'Reilly , a professor of cognitive neuroscience at the University of Colorado Boulder, has put the second edition of his textbook Computational Neuroscience online. I think it would be an excellent resource for you.
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.
He did this as an amateur without anything close to a related degree.
This kid discovered a new dinosaur,
Just google "high school student makes scientific discovery" or "college student makes scientific discovery" for a big list.
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.
Woo! Woo! Indians or curry Indians?
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
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
It is taught by some of the best in the field, and many alum have gone on to do good work.
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.
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
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.
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.
The HarvardX equivalent has already started with "Fundamentals of Neuroscience" It is a basic neuroscience course with a twist, they succeeded funding a Kickstarter 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.
That would be nice if all applied to "professional" meaning someone paid and "amateurs" as people not paid for their work. But it only really applies if you redefine it or pull some no true Scotsman stuff, and define professional as being those that create high quality stuff and amateurs as those that take shortcuts or fall short of high standards for whatever reason. Then you are stuck with something quite trite, "Keep it to yourself if it fails the arbitrary/ill-defined/conflicting standards of other random people on the internet."
What a nonsensical question.
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.
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
Emergent Neural network simulator. 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.
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.
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.
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). 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.
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.
Practice. A coworker once boggled at the brain's ability to calculate the physics necessary to toss a wad of garbage into a wastebasket, but it doesn't really work like that. Neuroplasticity allows the architecture of the brain to strengthen along the most frequently used pathways, so experience shapes your ability to predict the effects of your actions without having to understand the underlying mechanics.
would be useful for studying development of feature maps in the visual cortex. http://topographica.org/
I'm a neuroscience doctoral student studying epileptogenic networks. I would have messaged you if I could.
Even easier would be the case where someone touches his nose. Tactile information travels from the tip of the finger, along the arm, all the way to the brain. Information also travels from the tip of the nose to the brain. Two wildly different distances, yet we experience the same event. Clearly, there's some buffering going on here. The mechanism by which that happens is interesting enough to warrant its own research, don't you think?
thank U.. http://www.lorryguru.com/