marginalizing across spike histories

[agbondy]

--- This is a question, not an issue ---

For evaluating the goodness-of-fit of the model, it is useful to generate predictions that MARGINALIZE over all possible spike histories. That is, having fit the model to an observed spike train, one wants to evaluate how well it can explain the data without using the observed spike train as the spike history of the model. Otherwise, things become a bit circular. Anyone written code for this? Thanks!

[jpillow]

The simplest way is to just simulate repeated responses to the same stimulus, and calculate the mean (i.e. PSTH). That represents a prediction of spike rate conditioned on stimulus (with spike history marginalized out); this is what we did in eg Pillow et al 2008.

[agbondy]

Thanks for the quick reply, Jonathan! Yes, this is basically what I was imagining doing but hoping not to have to write the code myself ;)