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. |