I read the article by Naud, et al, in the December 2011 Neural Computation on developing similarity measures for spike trains. I've wrestled unsuccessfully with this problem in the past studying inferior colliculus neurons, and I'm happy to see the progress they've made. It does raise a question that came up in another area last summer--has anyone explored mixed models? That is, the model contains more than one specific spike train model. I've seen that approach applied in ecology, where the data have more than one pattern that depends on a stochastic or even deterministic control variable. If anyone has explored that kind of model, I'd be very interested. Note that such a model is probably ill-posed and hard to calibrate.
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Harry Erwin, PhD, Senior Lecturer of Computing, University of Sunderland. Computational neuroethologist:
http://crowan-scat.sunderland.ac.uk/~harryerw/mediawiki/index.php
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