Format:
1 Online-Ressource
ISSN:
1471-2202
Content:
Poster presentation: Introduction: The ability of neurons to emit different firing patterns is considered relevant for neuronal information processing. In dopaminergic neurons, prominent patterns include highly regular pacemakers with separate spikes and stereotyped intervals, processes with repetitive bursts and partial regularity, and irregular spike trains with nonstationary properties. In order to model and quantify these processes and the variability of their patterns with respect to pharmacological and cellular properties, we aim to describe the two dimensions of burstiness and regularity in a single model framework. Methods: We present a stochastic spike train model in which the degree of burstiness and the regularity of the oscillation are described independently and with two simple parameters. In this model, a background oscillation with independent and normally distributed intervals gives rise to Poissonian spike packets with a Gaussian firing intensity. The variability of inter-burst intervals and the average number of spikes in each burst indicate regularity and burstiness, respectively. These parameters can be estimated by fitting the model to the autocorrelograms. This allows to assign every spike train a position in the two-dimensional space described by regularity and burstiness and thus, to investigate the dependence of the firing patterns on different experimental conditions. Finally, burst detection in single spike trains is possible within the model because the parameter estimates determine the appropriate bandwidth that should be used for burst identification. ...
Note:
From Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18 - 23 July 2009
In:
BMC neuroscience, London : BioMed Central, 2000-, Band 10 (13. Juli 2009), Supplement 1, Artikel-ID: P246, 1471-2202
In:
volume:10
In:
year:2009
In:
day:13
In:
month:07
In:
supplement:Supplement 1
In:
extent:1
In:
elocationid:P246
Language:
English
DOI:
10.1186/1471-2202-10-S1-P246
URN:
urn:nbn:de:hebis:30-70931
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