Ben Shababo: May 1st


Title: Optimal Sequential Stimulation of Neural Populations For Inferring Functional Connectivity

Abstract: In this talk, we will review ongoing work in which we use methods from Bayesian experimental design, a subset of Active Learning, to guide an circuit mapping experiment. Specifically, the experimental paradigm we assume includes the recording of some output from a single cell - such as membrane voltage or current - and the ability to stimulate some subset of nearby neurons. The goal of the experiment is to learn the vector of weights that describe the influence of the cells we can stimulate on the cell we are recording from. In Bayesian experimental design the objective is to maximize the mutual information between the data and the parameters one wishes to learn which in turn entails a probabilistic model. For our model, we use a spike-and-slab prior on the weights with a linear gaussian likelihood. Furthermore, since this algorithm must perform in an online setting, we speed up the algorithm by approximating the optimization with a greedy version of the algorithm and by using online Bayesian updating of the posterior during stimulus selection. We will present results that show that within a specific regime our procedure outperforms random stimulation. We will also present some ideas we are currently incorporating into our model to make it more robust and applicable for the current state of experimental technology.