Dean Freestone: April 22nd

Title: Data-driven mesoscopic computational modeling Abstract: The talk will focus on two types of data-driven mesoscopic modeling. The first is known as neural field modeling, and the second neural mass modeling. It has been demonstrated that it is possible to estimate fast changing state variables (population firing rates or mean membrane potentials) and slowly changing parameters (connectivity strengths, time constants, and firing thresholds) from real electrophysiological data. The ability accurately estimate such quantities provides an opportunity to visualize aspects of brain function that is normally hidden when performing in-vivo studies. The talk will provide an update on efforts to improve and simplify estimation algorithms such that these ideas are more useful to the wider community.

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.

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.

Rajesh Ranganath: February 25th

Title: "Black Box Variational Inference"

Abstract: Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires significant model-specific analysis, and these efforts can hinder and deter us from quickly developing and exploring a variety of models for a problem at hand. We present a "black box" variational inference algorithm, one that can be quickly applied to many models with little additional derivation. Our method is based on a stochastic optimization of the variational objective where the noisy gradient is computed from Monte Carlo samples from the variational distribution. We develop a number of methods to reduce the variance of the gradient, always maintaining the criterion that we want to avoid difficult model-based derivations. We evaluate our method against the corresponding black box sampling based methods. We find that our method reaches better predictive likelihoods much faster than sampling methods. Finally, we demonstrate that Black Box Variational Inference lets us easily explore a wide space of models by quickly constructing and evaluating several models of longitudinal healthcare data.

Josh Merel: Feb 18th

ADADELTA and LSTMs

We will discuss the adadelta paper
http://arxiv.org/pdf/1212.5701v1.pdf

and talk about LSTM layers. The original paper is (for reference): http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
But we will probably discuss a more recent result using LSTM layers that uses a slightly different notation.

Uygar Sümbül: Feb 4th

Towards automated segmentation of neurons from Brainbow images

A necessary step to learn how neural circuits account for
observed behaviors in health and disease is to map the connectivity of
individual neurons. Stochastic expression of fluorescent proteins with
different colors where individual cells express one of many
distinguishable hues – the Brainbow method – has generated striking
images of nervous tissues. However, its use has been limited. One
basic shortcoming has been the various noise sources in fluorescence
expression within individual neurons. Here, we propose a method to
automate the segmentation of neurons in Brainbow image stacks using
spectral clustering.

 

Brian DePasqual: Jan 28

Embedding low-dimensional continuous dynamical systems in recurrently connected spiking neural networks

Despite recent advances in training recurrently connected firing-rate networks, the application of supervised learning algorithms to biologically plausible recurrently connected spiking neural networks remains a challenge. Such models, when trained to directly replicate neural data, hold great promise as powerful tools for understanding dynamic computation in biologically realistic neural circuits. In this talk I will discuss our progress in the training of recurrently connected spiking networks, the application of our training framework to neural population data and a novel interpretation of continuous neural signals that arises within the context of these models.

     Extending the iterative supervised learning algorithm of Sussillo & Abbott [2009], we have made several critical observations about the conditions necessary for successfully training recurrent spiking networks. Due to their impoverished short-term memory, multiple signals that form a “dynamically complete” basis must be trained simultaneously for successful training. I will illustrate this by showing a variety of examples of spiking neural networks replicating the dynamics of both autonomous and non-autonomous linear and non-linear continuous dynamical systems. Additionally, I will discuss recent efforts to incorporate a variety of network optimization constraints such that the learned connectivity matrices obey common constraints of biological networks, including sparsity and Dale’s Law. Finally, I will discuss our efforts to fit spiking models to population data from the isolated nervous system of the leech.

     Once trained, our models can be viewed as a low-dimensional, continuous dynamical system - traditionally modeled with firing-rate networks - embedded in a high-dimensional, spiking dynamical system. In light of this view, I will present a novel interpretation of firing-rate models and smoothly varying signals in general. Traditionally a continuous neural signal modeled as a “firing-rate unit” was a simplified representation of a pool of identical but noisy spiking neurons. In our formulation, each continuous neural signal represents an overlapping population of spiking neurons and is thus more akin to the multiple, continuous population trajectories one would uncover from experimental data via dimensionality reduction. By allowing these continuous signals to be constructed from overlapping pools of spiking neurons, our framework requires far fewer spiking neurons to arrive at the equivalent, traditional rate description.