Título:
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Direct estimation of inhomogeneous Markov interval models of spike-trains
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Autor/a:
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Wójcik, Daniel K.; Mochol, Gabriela; Jakuczun, Wit; Wypych, Marek; Waleszczyk, Wioletta J.
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Abstract:
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A necessary ingredient for a quantitative theory of neural coding is
appropriate “spike kinematics”: a precise description of spike trains.
While summarizing experiments by complete spike time collections is
clearly inefficient and probably unnecessary, the most common probabilistic
model used in neurophysiology, the inhomogeneous Poisson
process, often seems too crude. Recently a more general model, the inhomogeneous
Markov interval model (Berry & Meister, 1998; Kass &
Ventura, 2001),was considered,which takes into account both the current
experimental time and the time from the last spike. Several techniques
were proposed to estimate the parameters of these models from data.
Here we propose a direct method of estimation that is easy to implement,
fast, and conceptually simple. The method is illustrated with an analysis
of sample data from the cat’s superior colliculus. |
Abstract:
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We have benefited from discussions with Szymon Leski. This research is
funded by the Polish Ministry of Science and Higher Education, research
grants N401 146 31/3239 and 46/N-COST/2007/0. |
Derechos:
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© MIT Press (Publisher version at https://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.07-08-828)
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Tipo de documento:
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Artículo Artículo - Versión publicada |
Editor:
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MIT Press
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