Tuesday, September 29, 2015

Mayur Mudigonda: October 15th

Mayur Mudigonda is visiting from the Redwood Center at UC Berkeley. We will meet at 1pm on Thursday, October 15th, in room 502 NWC.

Title: Hamiltonian Monte Carlo Without Detailed Balance

We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions that satisfy the fixed point equation, but do not satisfy detailed balance. The resulting algorithm significantly suppresses the random walk behavior and wasted function evaluations that are typically the consequence of update rejection. We demonstrate a greater than factor of two improvement in mixing time on three test problems. We release the source code as Python and MATLAB packages. 


Friday, August 28, 2015

John Choi: September 3rd

Note that this seminar will be at 10:30am on Thursday, not the usual lab Wednesday lab meeting time.

 Title: Optimal Control for Developing Somatosensory Neural Prosthetics

Abstract: Lost sensations, such as touch, could one day be restored by electrical or optogenetic stimulation along the sensory neural pathways. Used in conjunction with next-generation prosthetic limbs, this stimulation could artificially provide cutaneous and proprioceptive feedback to the user. Microstimulation of somatosensory brain regions has been shown to produce modality and place-specific percepts, and while psychophysical experiments in rats and primates have elucidated the range of perceptual sensitivities to certain stimulus parameters, not much work has been done for developing encoding models for translating mechanical sensor readings to microstimulation. Particularly, generating spatiotemporal patterns for explicitly evoking naturalistic neural activation has not yet been explored. We therefore approach the problem of building a sensory neural prosthesis by first modeling the dynamical input-output relationship between multichannel microstimulation and subsequent field potentials, and then optimizing the input pattern for evoking naturally occurring touch responses as closely as possible, while constraining inputs within safety bounds and the operating regime of our model. In my work, I focused on the hand regions of VPL thalamus and S1 cortex of anesthetized rats and showed that such optimization produces responses that are highly similar to their natural counterparts. The evoked responses also preserved most of the information of physical touch parameters such as amplitude and stimulus location. This suggests that such stimulus optimization approaches could be sufficient for restoring naturalistic levels of information transfer for an afferent neuroprosthetic.

Josh Merel and Ari Pakman: August 19th

This week Josh and Ari will regale us with tales from their adventures at the recent Deep Learning Summer School in Montreal. They'll discuss trends and highlights and provide pointers to some interesting ideas.

Evan Archer: August 12th

For Wednesday's neurostat seminar I'll discuss three closely-related papers that appeared at ICML this year:

 • Variational Inference with Normalizing Flows 

Deep Unsupervised Learning using Nonequilibrium Thermodynamics 

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap 

Sunday, August 2, 2015

Daniel Soudry: July 29th

Daniel will discuss the following two papers, both concerning stochastic gradient Langevin dynamics:

 • Bayesian Sampling Using Stochastic Gradient Thermostats 

Dark Bayesian Knowledge 

Kishore Kuchibhotla: June 17th

Synaptic and circuit logic of task engagement in auditory cortex

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