Thursday, July 1, 2010
We have developed a simple model neuron for inference on noisy spike trains. In particular, we have in mind to use this model for computationally tractable quantification of information loss due to spike-time jitter. I will introduce the model, and in particular its favorable scaling properties. I'll display some results from inference done on synthetic data. Lastly, I'll describe an efficient scheme we devised for inference with a particular class of priors on the stimulus space that could be interesting outside the context of this model.