Maximum likelihood estimation of a stochastic
integrate-and-fire neural encoding model
Published in:
Neural Computation
16(12):2533-2561, December 2004
© MIT Press.
Related publications:
Previous conference publication of this work:
nips-03,
Application to retinal data:
JN-05,
A more general book chapter on characterizing neural responses:
Gazzaniga-03,
Description of biases in reverse-correlation (spike-triggered average) estimates of
the LNP model:
cns-02.
We examine a cascade encoding model for neural response in which a
linear filtering stage is followed by a noisy, leaky,
integrate-and-fire spike generation mechanism. This model provides a
biophysically more realistic alternative to models based on Poisson
(memoryless) spike generation, and can effectively reproduce a variety
of spiking behaviors seen in vivo. We describe the maximum
likelihood estimator for the model parameters, given only
extracellular spike train responses (not intracellular voltage data).
Specifically, we prove that the log likelihood function is concave and
thus has an essentially unique global maximum that can be found using
gradient ascent techniques. We develop an efficient algorithm for
computing the maximum likelihood solution, demonstrate the
effectiveness of the resulting estimator with numerical simulations,
and discuss a method of testing the model's validity using
time-rescaling and density evolution techniques.
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