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.

Yashar Ahmadian : August 11th

Yashar will be presenting preliminary work on applying random matrix theory to the study of transient dynamics in a non-normal linear neural network. 

Abstract:

The project is a collaboration with Ken Miller, and is motivated by his work on non-normal dynamics and transient amplification due to non-normality. I will give a brief background on this work

(see this paper: Balanced amplification: a new mechanism of selective amplification of neural activity patterns, by B.K. Murphy and K.D. Miller), and then give an expose of the diagrammatic method for calculating averages over a random (Hermitian N x N) matrix ensemble in the large N limit.

As an example, I will present how to derive the semi-circular law for Gaussian Hermitian matrices. 

Finally, I will discuss how one can extend the method to cover the non-normal case, and I will derive a formula for the spectral density in the large N limit.