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."

Comments

Re:Study and practice this in private.

[khallow]

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.

[bondsbw]

Not affiliated at all with Coursera, but I noticed this free course the other day. Starts in January.

Getting better access to information

[ODBOL]

(2) Is it easy for a non-academic to get the required data?

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:

1. 1. Get a community membership in the nearest university library. This should be cheap enough to be a no-brainer. It doesn't matter too much which university, because a lot of materials will be online through their Web catalogue, and there will be interlibrary loan. I'm not sure whether small 4-year colleges and community colleges have similar arrangements, but it's worth checking if one of those is much more convenient than a research university.
2. 2. Join a professional society, and/or the special interest group of a professional society, interested in your topic. Costs vary wildly, order of magnitude $100 to $1,000 per year. The Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE) probably have interest groups, and there are almost surely such groups within some cognitive science societies, but I am not familiar with those.
3. 3. Attend a conference sponsored by a group from step 2. This is likely to cost $1,000s when you add up travel, hotel, registration. If you have the time, and get lucky on location, you can save a bunch on travel. Saving by staying far from the conference hotel is usually a mistake. The value of the conference is less in attending talks than in meeting people, and having breakfast in the same hotel with everybody else can make a big difference. The success of unsolicited introductions will vary immensely across the people you try to meet, but you lose nothing but embarassment by trying. The main thing you can do to improve success is to avoid the Charybdis & Scylla of diffident awkwardness and bluff. Let on that you're an outsider, but don't downgrade whatever insight/ability/motivation you have. You'll probably have the greatest success meeting people who are networked in, but are not famous stars. That includes grad students and postdocs. It may be nice and polite to hang out with other outsiders, but probably not productive for your mutual goals.

Re:Computational neuro is more than simulations

[tylikcat]

+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.