Description: Detecting synfire chains in parallel spike data

This title appears in the Scientific Report : 2012

Detecting synfire chains in parallel spike data

The synfire chain model of brain organization has received much theoretical attention since its introduction (Abeles, 1982, 1991). However there has been no convincing experimental demonstration of synfire chains due partly to limitations of recording technology but also due to lack of appropriate a...

Personal Name(s):	Gerstein, G.L.
	Williams, E.R. / Diesmann, M. / Grün, S. / Trengove, C.
Contributing Institute:	Computational and Systems Neuroscience; INM-6
Published in:	Journal of neuroscience methods, 206 (2012) S. 54 - 64
Imprint:	Amsterdam [u.a.] Elsevier Science 2012
Physical Description:	54 - 64
DOI:	10.1016/j.jneumeth.2012.02.003
PubMed ID:	22361572
Document Type:	Journal Article
Research Program:	Brain-inspired multiscale computation in neuromorphic hybrid systems Theory, modelling and simulation Funktion und Dysfunktion des Nervensystems
Series Title:	Journal of Neuroscience Methods 206
Subject (ZB):	J Multiple spike trains Synfire chains Detection and analysis algorithms Repeating spike patterns Active identity intersection matrix
Link:	Get full text OpenAccess OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/7489 in citations.
Please use the identifier: http://dx.doi.org/10.1016/j.jneumeth.2012.02.003 in citations.

The synfire chain model of brain organization has received much theoretical attention since its introduction (Abeles, 1982, 1991). However there has been no convincing experimental demonstration of synfire chains due partly to limitations of recording technology but also due to lack of appropriate analytic methods for large scale recordings of parallel spike trains. We have previously published one such method based on intersection of the neural populations active at two different times (Schrader et al., 2008). In the present paper we extend this analysis to deal with higher firing rates and noise levels, and develop two additional tools based on properties of repeating firing patterns. All three measures show characteristic signatures if synfire chains underlie the recorded data. However we demonstrate that the detection of repeating firing patterns alone (as used in several papers) is not enough to infer the presence of synfire chains. Positive results from all three measures are needed.