Daniel will discuss the following two papers, both concerning stochastic gradient Langevin dynamics:
• Bayesian Sampling Using Stochastic Gradient Thermostats
• Dark Bayesian Knowledge
Sunday, August 2, 2015
Kishore Kuchibhotla: June 17th
Title:
Synaptic and circuit logic of task engagement in auditory cortex
Abstract:
Animals can adjust their behavior based on immediate context. A pedestrian will move rapidly away from traffic if she hears a car honk while crossing a street – executing a learned sensorimotor response. The same honk heard by the same pedestrian will not elicit this response if she is seated on a nearby park bench. How do neural circuits enable this type of behavior and flexibly encode the same stimuli in different contexts? Here we dissect the natural activity patterns of the same auditory stimuli in different contexts and show that attentional demands of a behavioral task transform the input-output function in auditory cortex via cholinergic modulation and local inhibition. Mice were trained to perform a go/no-go operant task in response to pure tones in one context (“active context”) and listen to the same pure tones but execute no behavioral response in another context (“passive”). In the active context, tone-evoked responses of layer 2/3 auditory cortical neurons were broadly suppressed when compared to the passive context but a specific sub-network showed increased activity. Neural responses shifted within 1-2 trials after the context switched. Whole-cell voltage clamp recordings in behaving mice showed larger context-dependent changes in inhibition than excitation, and the two sets of inputs sometimes changed in opposing directions. Attentional demands appear to reduce the necessity of co-tuned synaptic inputs, an otherwise established requirement in passive brain states. Task engagement elevated tone-evoked responses in PV-positive interneurons and suppressed VIP-positive interneuron responses, implicating both in the context-dependent changes to layer 2/3 output. Global behavioral context, in this case the attentional demands in the active context, was relayed to the auditory cortex by the nucleus basalis, as revealed by axonal calcium imaging of NB cholinergic projections. Thus, local synaptic inhibition gates long-range cholinergic modulation from NB to rapidly alter auditory cortical output, temporarily removing the requirement of co-tuned excitatory and inhibitory inputs, and improving perceptual flexibility.
Synaptic and circuit logic of task engagement in auditory cortex
Abstract:
Animals can adjust their behavior based on immediate context. A pedestrian will move rapidly away from traffic if she hears a car honk while crossing a street – executing a learned sensorimotor response. The same honk heard by the same pedestrian will not elicit this response if she is seated on a nearby park bench. How do neural circuits enable this type of behavior and flexibly encode the same stimuli in different contexts? Here we dissect the natural activity patterns of the same auditory stimuli in different contexts and show that attentional demands of a behavioral task transform the input-output function in auditory cortex via cholinergic modulation and local inhibition. Mice were trained to perform a go/no-go operant task in response to pure tones in one context (“active context”) and listen to the same pure tones but execute no behavioral response in another context (“passive”). In the active context, tone-evoked responses of layer 2/3 auditory cortical neurons were broadly suppressed when compared to the passive context but a specific sub-network showed increased activity. Neural responses shifted within 1-2 trials after the context switched. Whole-cell voltage clamp recordings in behaving mice showed larger context-dependent changes in inhibition than excitation, and the two sets of inputs sometimes changed in opposing directions. Attentional demands appear to reduce the necessity of co-tuned synaptic inputs, an otherwise established requirement in passive brain states. Task engagement elevated tone-evoked responses in PV-positive interneurons and suppressed VIP-positive interneuron responses, implicating both in the context-dependent changes to layer 2/3 output. Global behavioral context, in this case the attentional demands in the active context, was relayed to the auditory cortex by the nucleus basalis, as revealed by axonal calcium imaging of NB cholinergic projections. Thus, local synaptic inhibition gates long-range cholinergic modulation from NB to rapidly alter auditory cortical output, temporarily removing the requirement of co-tuned excitatory and inhibitory inputs, and improving perceptual flexibility.
Sunday, May 17, 2015
Patrick Stinson: May 20th
Abstract: I'll present Lindsten and Schoen's review of SMC-based backward simulation methods. The most immediate application of backward simulation is to address state smoothing problems in sequential models; however, this method can be generalized to non-Markovian latent variable models. Particle MCMC is a new method that incorporates SMC-based proposal schemes into MCMC algorithms. Backward simulation and a related method, ancestral sampling, can dramatically increase particle efficiency and mixing in this setting.
Paper: "Backward Simulation Methods for Monte Carlo Statistical Inference" by Fredrik Lindsten and Thomas B. Schoen
Link: http://users.isy.liu.se/en/rt/lindsten/publications/LindstenS_2013.pdf
Josh Merel: May 13th
Josh will give a recap of interesting happenings from the recent International Conference on Learning Representations (ICLR).
Friday, April 17, 2015
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.
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.
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.
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.
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