Tim Machado: July 3rd

Title: Functional organization of motor neurons during fictive locomotor behavior revealed by large-scale optical imaging

Abstract: The isolated neonatal mouse spinal cord is capable of generating sustained rhythmic network activity, termed fictive locomotion (Kiehn and Kjaerulff 1996, Markin et al. 2012). However, the spatiotemporal pattern of motor neuron activity during fictive locomotion has not been measured at single-cell resolution, nor has the variation across a motor pool been quantified. We have measured the activity of thousands of retrogradely labeled motor neurons using large-scale, cellular resolution calcium imaging. Spike inference methods (Vogelstein et al. 2010) have been used to estimate peak firing phase. This approach was validated in each experiment using antidromic stimulation of ventral roots to generate data where spike timing information is known. Our imaging approach has revealed that neurons within the same motor pool fire synchronously. In contrast, neurons innervating muscles that have slightly different phase tunings during walking also showed slightly offset burst times during fictive locomotion. Neurons innervating antagonist muscles reliably fired 180° out of phase with one another. Finally, groups of motor neurons that fired asynchronously were found at each lumbar spinal segment, suggesting that the recruitment of motor neurons during fictive locomotion is determined by pool identity, rather than by segmental position. These spatiotemporal patterns were each highly reproducible between preparations. Our approach has revealed complexity and specificity in the patterns of motor neuron recruitment during locomotor-like network activity. We are currently analyzing the relationship between the activity of genetically defined pre-motor interneurons and the activity of identified motor neuron pools.

José Miguel Hernández Lobato: June 27th

Title: Gaussian Process Vine Copulas for Multivariate Dependence

Abstract: Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this work, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

Special location: Mudd 210.

Roy Fox: June 26th

Title: KL-regularized reinforcement-learning problems

Abstract: Of the many justifications for regularizing reinforcement-learning problems with KL-divergence terms, perhaps the most obviously compelling is when it leads to efficient algorithms. This is the case under the assumptions of full observability and controllability, as in Emo Todorov's work on Linearly-Solvable Markov Decision Processes. In this talk I will present these ideas, mostly introduced in these two papers:
http://homes.cs.washington.edu/~todorov/papers/MDP.pdf
http://homes.cs.washington.edu/~todorov/papers/duality.pdf
Then I will share insights and challenges in applying similar approaches to partially observable and controllable MDPs.

Ari Pakman: June 19th

Title: Exact Hamiltonian Monte Carlo for Binary Distributions

Abstract: I will present a new approach to sample from generic binary distributions, based on an exact Hamiltonian Monte Carlo algorithm applied to a piecewise continuous augmentation of the binary distribution of interest. An extension of this idea to distributions over mixtures of binary and continuous variables permits sampling from posteriors of linear and probit regression models with spike-and-slab priors and truncated parameters.

Prof. Michael Shadlen: June 12th

Title: Firing rate autocorrelation as a signature of noisy evidence accumulation

Abstract: I plan to discuss insights about the nature of neural noise and computation. I will also address neural mechanisms involved in decision making and bring the two topics together by introducing new tools that reveal noisy evidence accumulation  (e.g., drift-diffusion) in the spike-trains of single neurons on single trials during decision formation.