Micky Vidne: October 27th. s(MC)^2 or Hesitant Particle Filter.

In my talk I will describe a recent extension of the Sequential Monte Carlo (SMC) method. SMCs (particle filters) are a commonly used method to estimate a latent dynamical process from sequential noise-contaminated observations. SMCs are extremely powerful but suffer from sample impoverishment, a situation in which very few different particles represent the distribution of interest. I will describe our attempt to circumvent this fundamental problem by adding an extra MCMC step in the SMC algorithm. I will illustrate the usefulness of this algorithm by considering a toy neuroscience example.

Jittering spike trains carefully

Presented in lab meeting by Alex Ramirez on July 14th, 2010.

A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains

by Matthew Harrison and Stuart Geman

In it the authors describe an algorithm that takes a spike train and jitters the spike times to create a new spike train which is maximally random while preserving the firing rate and recent spike-history of the original train.

Alex Ramirez : July 14

I'll be presenting a paper from Matt Harrsion.  In it the authors describe an algorithm that takes a spike train and jitters the spike times to create a new spike train which is maximally random while preserving the firing rate and recent spike-history of the original train.