Richard Norman <rsnorman at mediaone.net> wrote in message news:<evfnkt0bqb0v138jg6lil4pvi4s6n7udie at 4ax.com>...
> On 10 Jul 2001 17:57:48 -0700, jonesmat at physiology.wisc.edu (Matt
> Jones) wrote:
>> >"Isidore" <isidore at mailandnews.com> wrote in message news:<PoJ27.2$ln3.148 at typhoon.nyu.edu>...
> >> Hi everyone,
> >> I'm a high school student trying to read a neuroscience paper and
> >> understand it well. There are some keywords listed at the top of the page
> >> that I'm not exactly clear on.
>> Thanks, Matt. I knew someone else would come in who really knew
> something about spike train analysis. OK, so I was a math major as an
> undergraduate and in fact took courses on probablilty theory and
> random processes. But that was forty years ago! I do have a lot of
> experience, though, trying to get college sophomores and juniors
> through their first hard-core neuro papers. And I would never let one
> start out with spike train analysis (biology students usually have no
> math background or interest, unfortunately)
>> And, Isidore, I am also interested in what paper you have -- and how
> and why you got it. I'll bet this group could help steer you into a
> good reading program, if you have the time and interest.
Isidore confided the title of the paper to me by email.
I'll let him/her post it here, but suffice it to say it is -not- what
I would call light-hearted introductory reading material! It is a
-very- advanced and mathematical paper. I would be shocked if it was
actually an assigned paper, but if it was, I'd like to know what the
rest of the course is like!
I think it's awesome, by the way, that a high school student is even
reading a serious research paper, let alone such a hardcore one, and
is actively seeking additional knowledge about the subject not covered
in the textbook. Nice one, Isidore.
Anyway, I recommended a couple of other works that give a (slightly)
more gentle introduction, and those I will post here, with brief
comments, in hopes of stimulating additional discussion here about
spiketrain analysis, neural coding and (dare I say it) information
theory. So, anybody with 2 cents, please feel free to pipe up with
comments or suggestions of your own.
Konig, P, A K Engel and W Singer (1996). "Integrator or
coincidence detector? The role of the cortical neuron
revisited." Trends in Neuroscience 19: 130-137.
This is very nice review of the issues surrounding "leaky
integrate-and-fire" devices and such. I found it very readable.
Shadlen, M N and W T Newsome (1998). "The variable discharge of
cortical neurons: implications for connectivity, computation, and
information coding." J Neurosci 18: 3870-96. This is a pretty long
paper, but it does a pretty good job of laying out a lot of the issues
surrounding spiketrain analysis, and the kinds of things people
measure, and how they interpret them. These authors are "rate code"
guys, and argue very forcefully that spike timing is completely
Rieke, F, D Warland, R de Ruyter van Steveninck and W
Bialek (1997). Spikes: Exploring the neural code. Cambridge,
MA, MIT Press.
This is a book, not a paper. You -might- be able to get it a local
university library. It's out now in paperback, and can be ordered from
the MIT press website or avalon.com. This is by far my favorite work
on the whole issue of understanding neural coding. Chapters 1 and 2
give a fantastic description of most of the common methods of
spiketrain analysis, and much of the rest of the book is devoted to
exploring the idea that spike timing and patterns convey a huge amount
of information that is missed if one only examines spike rate. If you
decide that you actually want to develop a deeper understanding of
these issues (as opposed to a short term school project or whatever),
then buy this book and read it. There's a fair bit of math, but most
of the hard stuff is stashed away in an appendix, while the main body
of the text is mostly in plain english.
I also remembered a book chapter by Gabbianni and Koch, I think called
"spiketrain analysis" or something like that. I can't remember the
actual reference at the moment but this was a really good one too.
And there's a whole book on computational neuroscience by Dayan and
Abbott. I haven't read it, but have browsed it on the web and it looks