regularity of spike series

k p Collins kpaulc at [----------]earthlink.net
Sun Jan 25 01:54:35 EST 2004


=Please= Forgive me, but 'time' correlations are
absolutely nothing, if the globally-integrated neural
Topology is not integrated, also, within the local
analysis.

Activation within the hippocampus is, in fact, an
extreme case in-point, because, since the hippo-
campus functions as a "supersystem configuration"
mechanism [primarily with respect to the thalamus,
but also with respect to the hypothalamus and
amygdala, and many other 'less-significant' 'zones'
within the neural Topology], the activation that
occurs within the hippocampus, necessarily, can
be =anything=. So, doing a 'time' analysis on data
that's exclusively-correlated to the hippocampus,
can tell one absolutely nothing about what's going
on within the hippocampus.

Again, Please Forgive me.

The analysis can =only= be successfully accomplished
through resort to the globally-integrated neural Topology.

I am not 'criticizing'. I'm just trying to get the Necessity
inherent across to folks.

ken [k. p. collins]

"Glen M. Sizemore" <gmsizemore2 at yahoo.com> wrote in message
news:052194e1be4a102e7ff47c009947d5b0 at news.teranews.com...
> Mat: Autocorrelograms can reveal simple regularities in spike trains, e.g.
> if many spikes are followed by another one at 30s then you'll get a
> bump in the autocorrelogram. But since I've not found anything like
> that what I need is a measure of higher-order patterns, though I'm not
> sure even if I find one how to interpret that since it might be highly
> non-intuitive.
>
> GS: Back up. What does the inter-event interval distribution look like?
Make
> sure you plot the data using the right "bin size;" too large a bin and
> multiple modes can be obscured. You don't want the bin size to be too
small
> either. The distribution is the first place to look for "simple
regularities
> in spike trains." I don't think it is necessarily true that "...if many
> spikes are followed by another one at 30s then you'll get a bump in the
> autocorrelogram," is it? If we construct a time-series by randomly drawing
> from a normal distribution centered at 30 s, we would not see a reliable
> bump anywhere when we plotted an autocorrelogram constructed from the
> time-series, as there would be no sequential dependencies - the duration
of
> an event at n has no bearing on the duration at n+x, no? Am I missing
> something? Plot the distribution of inter-event intervals, then calculate
> the inter-event interval per opportunity (IEI/op). That is, plot how many
> IEIs occurred in a particular bin and divide it by the number of IEIs in
> that bin plus all those in the bins greater than it. Plot this number as a
> function of ordinal position of the bin (and, of course, the ordinal
> position is related to time as that is the dimension of the bin). This
> yields a conditional probability at each discrete "point" in time
> (obviously, the accuracy depends on the size of the bin). It expresses the
> probability that an event will follow the previous event by an amount of
> time defined by the ordinal position of the bin. This tells you something
> about the likelihood that an event will occur at any point following
another
> event. If you don't get any reliable auto-correlation at any lag, it means
> that the distribution specifies everything about the temporal properties
of
> the train of events that is possible under those conditions. No?
>
> "mat" <mats_trash at hotmail.com> wrote in message
> news:43525ce3.0401240446.563ead22 at posting.google.com...
> > y.k.y at lycos.com (yan king yin) wrote in message
> news:<72de81ae.0401231727.6564dfcf at posting.google.com>...
> > > Spike trains obtained from a single neuron is the
> > > result of dendritic integration of ~1000 to 100,000
> > > of other neurons' inputs. What kind of information can
> > > autocorrelation of a few spike trains reveal? Probably
> > > very little...
> > >
> > > I guess your research will be more fruitful if you
> > > focus more on single neuron information processing.
> > >
> >  These are not single neuron spikes they are field potentials.  I'm
> > using an in vitro model of epileptic activity by perfusing magnesium
> > free artificial CSF.  What I would like to establish is whether there
> > is any degree of regularity to these trains of depolarizations
> > (~6-10/min) and see if this is altered when I co-perfuse drugs across
> > the cortex.
> >
> > Autocorrelograms can reveal simple regularities in spike trains, e.g.
> > if many spikes are followed by another one at 30s then you'll get a
> > bump in the autocorrelogram.  But since I've not found anything like
> > that what I need is a measure of higher-order patterns, though I'm not
> > sure even if I find one how to interpret that since it might be highly
> > non-intuitive.
>
>





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