regularity of spike series

k p Collins kpaulc at [----------]earthlink.net
Tue Jan 27 16:43:38 EST 2004


Hi Matt,

"Matt Jones" <jonesmat at physiology.wisc.edu> wrote in message
news:b86268d4.0401271126.7bd0407c at posting.google.com...
> > [...]
> [...]

> There have been several papers about
> detecting higher order structure in
> neuronal time series that you might
> find useful. You may want to check
> out these papers, which use return
> plots to study dynamics in slices
> under "epileptic" conditions:
>
> Schiff SJ, Jerger K, Duong DH, Chang
> T, Spano ML, Ditto WL. Controlling
> chaos in the brain. Nature. 1994 Aug
> 25; 370(6491): 615-20.
>
> Aitken PG, Sauer T, Schiff SJ.
> Looking for chaos in brain slices.
> J Neurosci Methods. 1995 Jun; 59(1): 41-8.
>
> Slutzky MW, Cvitanovic P, Mogul DJ.
> Manipulating epileptiform bursting in the
> rat hippocampus using chaos
> control and adaptive techniques.
> IEEE Trans Biomed Eng. 2003 May;
> 50(5): 559-70.
>
> Slutzky MW, Cvitanovic P, Mogul DJ.
> Deterministic chaos and noise in three in
> vitro hippocampal models of epilepsy.
> Ann Biomed Eng. 2001; 29(7): 607-18.

I won't be able to demonstrate it until
someone allows me to spend a week,
so working with their data, but my
experience if that folks who see chaos
are usually looking at data that's only
partial.

That is, the data sees only part of what
is always a larger dataset, and, when the
larger dataset is analyzed it always ex-
hibits one general trend that goes from
disorder to order. The high-frequency
components decrease both in number
and relative-power, which means that
there's an information-processing dyn-
amic that's converging to 'solidity'.

The other thing that makes the analysis
difficult is that there are always many
such convergences proceding in parallel,
and they tend to mask each other's trends.

This difficulty can always be sorted-out,
however, by collecting data that's
'anchored' to specific effectors [specific
motor directionality] that are [is] rela-
tively-widely-separated in functionality.
[forelimb vs. hindlimb, etc.].

The 'anchors' allow their correlated
convergences to be extracted from
within the multi-parallelism.

No "chaos". Just overlapping multi-
parallelism of the convergences.

["TD E/I-minimization".]

Cheers, ken [k. p. collins]





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