Tuesday, March 17, 2015

Johannes Friedrich: April 1st

Title: Goal-directed decision making with spiking neurons

Abstract: Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, which requires extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way, and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a remarkably simple neural network to achieve optimal performance, and solves one-step decision making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision making tasks within a second. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, while the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision making tasks with multiple rewards.

Monday, March 16, 2015

Scott Linderman: March 18th

Title: Discovering latent structure in neural spike trains with negative binomial generalized linear models

Abstract: The steady expansion of neural recording capability provides exciting opportunities to discover unexpected patterns and gain new insights into neural computation. Realizing these gains requires statistical methods for extracting interpretable structure from large-scale neural recordings. In this talk I will present our recent work on methods that reveal such structure in simultaneously recorded multi-neuron spike trains. We use generalized linear models (GLM’s) with negative-binomial observations, which provide a flexible model for spike trains. Interpretable properties such as latent cell types, features, and hidden states of the network are incorporated into the model as latent variables that mediate the functional connectivity of the GLM. We exploit recent innovations in negative binomial regression to perform efficient Bayesian inference using MCMC and variational methods. We apply our methods to neural recordings from primate retina and rat hippocampal place cells.