Abstract:
: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.
journal_name
Neural Computjournal_title
Neural computationauthors
Wójcik DK,Mochol G,Jakuczun W,Wypych M,Waleszczyk WJdoi
10.1162/neco.2009.07-08-828subject
Has Abstractpub_date
2009-08-01 00:00:00pages
2105-13issue
8eissn
0899-7667issn
1530-888Xjournal_volume
21pub_type
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