Current meeting time: Wednesdays, 11:301:00pm (PNI 511).
Current Schedule
 Feb 28: cosyne poster review
 Mar 7: no meeting (Cosyne)
 Mar 14: Anqi Wu
 Mar 21: Jonathan Pillow
Past Meetings
 Feb 21: Brian DePasquale – Motor Cortex Embeds Musclelike Commands in an Untangled Population Response Russo, Bittner, Perkins, Seely, London, Lara, Miri, Marshall, Kohn, Jessell, Abbott, Cunningham, & Churchland. Neuron 2018.
 Feb 14: Farhan Damani – Fast epsilonfree Inference of Simulation Models with Bayesian Conditional Density Estimation. Papamakarios & Murray. NIPS 2016.
 Feb 7: Mikio Aoi – Bayesian group latent factor analysis with structured sparsity. Zhao, Gao, Mukherjee, & Engelhardt BE. JMLR (2016).
 Jan 31: Ben Cowley (visiting speaker)
 Jan 17: Gabriel Barello (visiting collaborator) – informal talk on GSMs
2017
 Dec 13: Mike Morais – Compressed Gaussian Process for Manifold Regression.
Guhaniyogi & Dunson. JMLR (2016)  Dec 6: Nick Roy – Dynamic Routing Between Capsules Sabour, Frosst, & Hinton. arxiv (2017)
 Nov 29: Stephen Keeley – Automatic differentiation in machine learning: a survey.Baydin, Pearlmutter, Radul, & Siskind, arxiv (2015). [summary]
 Nov 15: no meeting (SFN)
 Nov 8: Brian De Pasquale – Stochastic variational learning in recurrent spiking networks. DJ Rezende & W Gerstner. Frontiers in Comp. Neurosci. 2014. [summary]
 Nov 2: Siwei Wang (Hebrew U.)
 Oct 25: Josh Glaser, Northwestern University (11am12pm in room 101)
 Oct 18: Anqi Wu – Learning Scalable Deep Kernels with Recurrent Structure. AlShedivat, Wilson, Saatchi, Hu, & Xing; JMLR 2017. [summary]
 Oct 11: Jonathan – PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. Huggins, Adams, & Broderick, NIPS 2017
 Oct 4: Adam Charles – Deterministic Symmetric Positive Semidefinite Matrix
Completion, Bishop & Yu, NIPS 2014. [summary]  Sept 27: Alex Hyafil – research talk: “Looking for a bit of attention: a reanalysis of the motionpulse monkey data”.
 Sept 20: Mike Morais – Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control. Pan, Yan, Theodorou, & Boots, ICML 2017
 Sep 6: Roger She – Learning Stable Stochastic Nonlinear Dynamical Systems
Umlauft & Hirche, PMLR 70, 2017

Aug 31: Nick Roy – Understanding Blackbox Predictions via Influence Functions. Pang Wei Koh & Percy Liang, arxiv 2017
 Aug 23: Mikio Aoi – new ideas on modeling of scalar variability
 Aug 16: Stephen Keeley – StimulusDriven Population Activity Patterns in Macaque Primary Visual Cortex. Cowley, Smith, Kohn, & Yu. PLoS comp bio 2016.
 July 26: Lea Duncker – “Sparse Variational Gaussian Processes for NonConjugate Latent Factor Models”
 July 5: Brian DePasquale – Variational Sequential Monte Carlo, Naesseth, Linderman, Ranganath, & Blei, arxiv (2017)
 June 21: VAE hackathon (all day)
 June 14: Jonathan – Inferring hidden structure in multilayered neural circuits,
Maheswaranathan, Baccus, & Ganguli, biorxiv (2017)

June 7: round robin
 May 24: David Zoltowski – reparametrization trick for discrete latent variable models:
1) The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. Maddison, Mnih, & Teh (2017).
2) Categorical Reparameterization with GumbelSoftmax. Jang, & Gu, Poole (2017)  Apr 26: Mikio Aoi – Distance Covariance Analysis. Cowley, Semedo, Zandvakili, Smith, Kohn, & Yu. AISTATS (2017)
 Apr 19: Adam Charles – Fast direct methods for Gaussian processes. Ambikasaran et al, IEEE trans. pattern analysis and machine intelligence 38.2 (2015)
 Apr 12: Mike Morais – Dimensionality reduction preserving class dependent information (rotation project talk)
 April 4 @ noon: joint mtg with Michael Berry group.
 Mar 27: Gamaleldin Elsayed (visiting speaker)
 Mar 22: Anqi Wu – Kernel Interpolation for Scalable Structured Gaussian Processes (KISSGP), Wilson & Nickisch ICML,2015
 Mar 8: Cosyne wrapup
 Feb 8: Nick – Learning to Reinforcement Learn, Wang et al 2017. [See also: RL2: Fast Reinforcement Learning via Slow Reinforcement Learning, Duan et al 2016.]
 Feb 1: Stephen Keeley – Neural Circuit Inference from Function to Structure. Real, Asari, Gollisch, & Meister. Current Biology 2017
 Jan 25: Brian DePasquale – Adversarial Autoencoders. Makhzani, Shlens, Jaitly, Goodfellow, Frey, Arxiv 2015.
 Jan 18: David Zoltowski Composing graphical models with neural networks for structured representations and fast inference, Johnson, Duvenaud, Wiltschko, Datta, & Adams. Arxiv 2016.
 Jan 11: Mikio Aoi “Lowdimensional, dynamic encoding in prefrontal cortex during decisionmaking” (practice talk)
2016:
 Dec 14: Mikio – Recurrent switching linear dynamical systems
Linderman, Miller, Adams, Blei, Paninski, & Johnson. Arxiv 2016  Nov 23: Adam: Deep Learning Models of the Retinal Response to Natural Scenes, McIntosh, Maheswaranathan, Nayebi, Ganguli, & Baccus. NIPS 2016
 Nov 2: SVI bootcamp (Anqi)
 Oct 26: Anqi – Stochastic Variational Inference. Hoffman, Blei, Wang & Paisley, JMLR 2013.
 Oct 19: Jonathan – research talk: “Bayesian Efficient Coding” (ongoing work with Memming Park)
 Oct 12: Nick: Humanlevel control through deep reinforcement learning. Mnih et al, Nature (2015).
 Oct 5: David: “On the role of time in perceptual decisionmaking.” talk on recent work with Maté Lengyel.
 Sep 28: Mikio: Interpretable Nonlinear Dynamic Modeling of Neural Trajectories. Zhao & Park (NIPS 2016)
 Sep 21: Mike Shvartsman: Inferring mental states jointly from brain and behavior.
 Sep 15: Brian – LFADS – Latent Factor Analysis via Dynamical Systems. Sussillo, Jozefowicz, Abbott & Pandarinath
Sep 7: Nirag Kadakia (UCSD), visiting speaker  Aug 30 (Tues): Lea – research talk
 Aug 24: Lea – Linear dynamical neural population models through nonlinear embeddings. Gao, Archer, Paninski, & Cunningham. arxiv 2016.
 Aug 17: Jonathan – Tutorial on Variational Autoencoders
Carl Doersch. arxiv 2016  July 6: Adam – Compressive sensing. Reading: An introduction to compressive sampling. Candès & Wakin, IEEE Signal Processing Magazine (2008)
 June 29: Jonathan – Fast Sequences of Nonspatial State Representations in Humans. KurthNelson, Economides, Dolan, & Dayan. Neuron (2016)
 June 22: joint lab mtg with Murthy Lab
 June 15: Lea – two papers on GP latent variable methods:
1) Bayesian Gaussian Process Latent Variable Model. Titsias & Lawrence, ICML (2010).
2) Variational Inference for Uncertainty on the Inputs of Gaussian Process
Models. Damianou, Titsias, & Lawrence. arxiv (2014)  June 8: Research Talk: Mikio – “Bayesian targeted dimensionality reduction.”
 May 11: visiting speaker: Stéphane Deny
 May 4: Research Talks: Anqi on Convolutional subunit models & Jonathan on conductancebased interpretation and extensions of GLM.
 Apr 27: Mikio: Bayesian structure learning for stationary time series. Tank, Foti & Fox. arxiv 2015. [summary]
 Apr 20 (2pm w/ Murthy, Witten, Shaevitz groups): Mapping SubSecond Structure in Mouse Behavior. Wiltschko, Johnson, Iurilli, Peterson, Katon, Pashkovski, Abraira, Adams, & Datta. Neuron (2015). [summary]
 Apr 13: Research Talks: Adam & Ji Hyun
 Apr 6: Stephen Keeley (visiting speaker)
 Mar 30 (10:30am): Jonathan – Visualizing data using tSNE. Van der Maaten & Hinton. JMLR (2008)
 Mar 23: Ji Hyun – Learning In Spike Trains: Estimating WithinSession Changes In Firing Rate Using Weighted Interpolation. Jensen, Munoz, & Ferrera. bioRxiv (2016) [summary].
 Mar 9: Cosyne highlights
 Feb 17: Mikio: practice talk on targeted dimensionality reduction
 Feb 2: Nick & Alice: rotation project presentations.
 Jan 19: Lea – internal talk: Nuclearnormpenalised multivariate timeseries autoregression. (Note special time: 11a12p).
 Jan 12: Anqi – Autoencoding variational bayes. Kingma & Welling, arxiv 2013
Fall 2015:
 Dec 15: nips highlights.
 Dec 8: no meeting (nips)
 Dec 1: Adam: Randomly connected networks have short temporal memory. Wallace, Hamid, & Latham. Neural Computation (2013). [summary]
 Nov 24 @ 11:30am: Andy Liefer (joint meeting with Berry group
 Nov 17: rotation students’ project updates (round robin)
 Nov 10: Angela: The reusable holdout: Preserving validity in adaptive data analysis. Dwork et al, Science 2015.
 Oct 27: Mikio (joint w/ Berry group) – How Can Single Sensory Neurons Predict Behavior? Pitkow, Liu, Angelaki, DeAngelis, & Pouget, Neuron 2015
 Oct 13: Jonathan: discussion of models for overdispersed spike counts, starting off from Goris et al, ‘Partitioning Neuronal Variability’, NN 2014.
 Oct 6: joint meeting with Berry lab: Kanaka Rajan presenting recent work on “Recurrent network model of sequence generation”. (Tues at 12pm).
 Oct 2: Adam – Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. Saxe, McClelland, & Ganguli 2014. arXiv:1312.6120 [summary].
 Sept 22: Anqi – Deep Exponential Families. Ranganath, Tang, Charlin, & Blei. arXiv:1411.2581. [summary].
SpringSummer 2015:
 July 27: Mikio – A categoryfree neural population supports evolving demands during decisionmaking. Raposo, Kaufman & Churchland, Nat Neurosci 2014. [summary]
 July 13: Adam Charles: internal talk on sparse dynamic filtering (Ph.D. work)
 July 7: round robin
 June 8: Jake – internal talk on current MTLIP modeling & analysis
 May 19: Angela – internal talk on decisionmaking
 May 11: Anqi – Fast Kernel Learning for Multidimensional Pattern Extrapolation. Wilson, Gilboa, Nehorai, & Cunningham. NIPS 2014
 May 4: no mtg (PNI retreat)
 Apr 27: Mikio – Robust spectrotemporal decomposition by iteratively reweighted least squares. Ba, Babadi , Purdon, & Brown. PNAS 2014. [summary]
 Apr 20: Kenneth: Modeling the auditory scene: predictive regularity representations and perceptual objects. Winkler, Denham, & Nelken. Trends in Cog Sci 2009.
 Apr 13: Jonathan – From fixed points to chaos: Three models of delayed discrimination. Barak, Sussillo, Romo, Tsodyks, & Abbott, Prog. in Neurobio 2013. [summary]
 Apr 6: Jake – Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT. Wimmer et al, Nature Communications 2015. [summary]
 Mar 30: DJ Strouse – deterministic information bottleneck.
 Mar 23: Anqi – Extracting spatialtemporal coherent patterns in largescale neural recordings using dynamic mode decomposition. Brunton, Johnson, Ojemann, & Kutz, arxiv 2014. [summary]
 Mar 16: Cosyne review.
2014
 Nov 24: Scott Linden – learning synaptic plasticity rules from spike trains.
 Nov 17: Jonathan: A Framework for Testing Identifiability of Bayesian Models of Perception. Acerbi, Ma, Vijayakumar, NIPS 2014.
 Nov 11: Memming – Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Vincent et al JMLR 2010.
 Nov 3: Kate – Attention can either increase or decrease spike count correlations in visual cortex, Ruff & Cohen, NN (2014).
 Oct 27: Kenneth – Understanding predictive information criteria for Bayesian models, Gelman, Hwang, & Vehtari (2014).
 Oct 21: Anqi – Bayesian Structured Sparsity from Gaussian Fields, Engelhardt & Adams 2014.
 Oct 14: Memming – Stochastic backpropagation and approximate inference in deep generative models, Rezende, Mohamed, & Wierstra, D. (2014).
 Aug 11: Jake – Quantifying the effect of intertrial dependence on perceptual decisions, Fründ, Wichmann & Macke, JOV 2014.
 July 28: Jonathan – Efficient and direct estimation of a neural subunit model for sensory coding, Vintch, Zaharia, Movshon, & Simoncelli, NIPS 2012. [summary]
 July 21: Anqi – Fast Marginal Likelihood Maximisation for Sparse Bayesian Models, Tipping & Faul, AISTATS 2003. [summary]
 July 14: Memming – Noise correlation in V1: 1Ddynamics explains differences between anesthetized and awake. Ecker et al., Neuron 2014. [summary]
 July 7: Jake & Karin – “Partitioning neuronal variability.” Goris, Movshon & Simoncelli, NN 2014. [summary].
 June 30: Kenneth – “Brain circuits underlying visual stability across eye movements—converging evidence for a neurocomputational model of area LIP.”
Ziesche and Hamker, Front Comp Neurosci 2014.  June 23: Kate – “Explaining human multiple object tracking as resourceconstrained approximate inference in a dynamic probabilistic model“. Vul, Alvarez, Tenenbaum, & Black, NIPS 2009. [summary]
 June 16: Karin – “all of dynamics in 10 minutes”
 June 9: Keegan – nonparametric Bayesian methods for modeling ion channels
 June 2: Round robin – round robin on NIPS projects (Memming, Anqi, Kenneth, Karin, Jonathan)
 May 12: Jake – practice talk on LIP and MT data
 May 6: Kenneth – research talk: “Unraveling the dynamics of decision making in area LIP”
 Apr 29: Jonathan – “1 vs. 2 neurons— notes on a paradox in choice probability”
 Apr 21: David Pfau, visiting speaker: “Learning Structure in Time Series for Neuroscience and Beyond”
 Apr 15: Kenneth two papers on improved methods for evaluating point process likelihoods: (1) Mena & Paninksi 2014: “On quadrature methods for refractory point process likelihoods“; (2) Citi, Ba, Brown & Barbieri 2014: “Likelihood Methods for Point Processes with Refractoriness” [summary].
 Mar 8: Kate – research talk
 Mar 1: Jake – Adaptive Allocation of Attentional Gain, Scolari & Serences, JN 2009. [summary — Jake, we’re waiting!]
 Mar 25: Memming – dimensionality reduction of neural data, part II
 Mar 18: Karin – research talk
 Mar 11: spring break
 Mar 7: (special date with Evan & Mijung) – Cosyne highlights
 Mar 4: Cosyne Worksops (See highlights of our workshop on largescale models)
 Feb 25: Cosyne practice
 Feb 18: cancelled
 Feb 11: Memming – dimensionality reduction of neural data
 Feb 4: Kate: final thoughts on dimensionality reduction, gating, linearization of dynamics in artificial neural networks
 Jan 27: Kate: Contextdependent computation by recurrent dynamics in prefrontal cortex. Mante, Sussillo,Shenoy, & Newsome 2013 (Part Deux) [summary].
 Jan 13: Evan: latent dynamical models with quadratic inputs (joint work with Jakob Macke)
2013
 Dec 17 – NIPS wrapup
 Dec 3 – NIPS practice poster presentations
 Nov 26 – Kate: Contextdependent computation by recurrent dynamics in prefrontal cortex. Mante, Sussillo,Shenoy, & Newsome 2013
 Nov 12 – Mijung: Active Learning of Linear Embeddings for Gaussian Processes. Garnett, Osborne & Hennig 2013
 Nov 5 – Karin: A unified framework and method for automatic neural spike identification, Ekanadham, Tranchina & Simoncelli 2013. [summary]
 Oct 29 –Jake (research Talk)
 Oct 22 – highlights of Grossman Center Workshop (Kate & Jonathan)
 Oct 8 – round robin (Johannes & Jake discuss “natural scene encoding”)
 Oct 1 – Jake: statistical issues in Poisson regression
 Sep 24 – Mijung: Research Talk
 Sep 17 – Jonathan: Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trains. Smith & Paninski, Network 2013 (pdf)
 Sep 10 – Kate: rotation talk on RBMs
 July 30 – Kenneth: Analysis of the Context Integration Mechanisms Underlying Figure–Ground Organization in the Visual Cortex, Zhang & von der Heydt. JN 2010
 July 23 – Jake: More is not always better: adaptive gain control explains dissociation between perception and action. Simoncini et al, NN 2012
 July 9 – Karin: Recovery of Sparse TranslationInvariant Signals with Continuous Basis Pursuit. Ekanadham, Tranchina, & Simoncelli 2011
 July 2 – Memming, practice talk.
 June 25 – Kate: Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest. Larochelle, Bengio & Turian, Neural Comp 2010 [summary]
 June 18 – Jonathan: Efficient coding of spatial information in the primate retina. Doi et al, J. Neurosci 2012.
 June 10 – Memming: Hessianfree optimization (Martens 2010). [summary].
 June 3 – roundrobin discussion / summer research ideas
 May 20 – NIPS papers roundup & progress reports
 May 13 – Kenneth: GPU programming Matlab / CUDA
 May 6 – no lab mtg (Jonathan & Memming in Columbus, Ohio)
 Apr 29 – Karin & Tejas: Latent Variable Bayesian Models for Promoting Sparsity. Wipf Rao & Nagarajan, IEEE trans info theory 2011
 Apr 22 – Tejas: Dual space analysis of the sparse linear model. Wipf & Wu, NIPS 2012.
 Apr 15: Evan – Hierarchical spike coding of sound. Karklin, Ekanadham & Simoncelli, NIPS 2012.
 Apr 1: Mijung – Bayesian inference for GLMs with expectation propagation (EP). (Seeger et al 2007; Gerwinn et al, 2010) [summary]
 Mar 25: Memming – Demixed PCA by Brendel, Romo, Machens. NIPS 2011 [summary]
 Mar 18: Cosyne wrap up
 Mar 4: no lab meeting (Cosyne) [summary]
 Feb 25, 2013 Cosyne practice
 Feb 11: Jonathan – Spectral Methods: tutorial slides from Gordon, Song & Boots, ICML 2012. [summary]
 Feb 04: Memming – Wang, Stocker & Lee (NIPS 2012), Optimal neural tuning curves for arbitrary stimulus distributions: Discrimax, infomax and minimum Lp loss [summary]
 Jan 28: – organizational / round robin (brief review of current projects).
2012
 Sep 17, 2012 – Karin – Wood, Archambeau, Gasthaus, James, & Teh, A Stochastic Memoizer for Sequence Data. ICML, 2009. [summary]
 Sep 10, 2012 – Jake & Kenneth – Berens et al., A Fast and Simple Population Code for Orientation in Primate V1. J. Neurosci 2012.
 Aug 27, 2012. Round Robin (+ highlights from Aspen Brain Forum 2012 mtg)
 Aug 20, 2012. Memming – Discussion on statespace models and online algorithms
 Aug 13, 2012. Mijung – Discussion on overdispersed PoissonGP inference
 July 30, 2012. Evan – Fournier et al, 2011. Adaptation of the simple or complex nature of V1 receptive fields to visual statistics
 July 23, 2012. Kenneth – Paninski et al. Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods.
 July 16, 2012. Jake – Fitzgerald et al. Symmetries in stimulus statistics shape the form of visual motion estimators.
 July 9, 2012. Jonathan.
 July 2, 2012. Memming – preliminary results on decoding of V1 population data.
 June 25, 2012. Mijung – Automating the design of informative sequences of sensory stimuli, Lewi, Schneider, Woolley & Paninski. JCNS 2011. [summary]
 June 18, 2012. Evan – expected loglikelihood trick for generalized quadratic models.
 June 11, 2012. Jonathan – digression on “singlespike information” (Brenner et al, 2000).
 June 4, 2012. Abhinav – Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability. Beck, Ma, Pitkow, Latham, & Pouget. Neuron 2012.
 May 21, 2012.Kenneth – quals practice talk.
 May 14, 2012. Round robin – NIPS papers progress report.
 April 30, 2012. Memming – olfactory coding in lobster.
 April 23, 2012. Jonathan. Efficient coding.
 April 16, 2012. Jake – Hierarchical processing of complex motion along the primate dorsal visual pathway. Mineault, P., & Khawaja, F. PNAS (2012).
 April 9, 2012. Jonathan – Statistical inference for noisy nonlinear ecological dynamic systems. S. Wood. Nature 466, 1102–1104 (2010)
 April 2, 2012. Evan – Improved predictions of lynx trappings using a biological model. Reilly and Zeringue, A. 2004. (See discussion on Gelman’s blog). [summary]
 Mar 26, 2012. Kenneth – Riemann manifold HMC paper, part II. [summary]
 Mar 19, 2012. Kenneth – Riemann manifold Langevin and Hamiltonian Monte Carlo methods. Mark Girolami & Ben Calderhead JRSSB 2011
 Mar 05, 2012 Cosyne highlights / discussion
 Feb 27, 2012 at Cosyne (no meeting)
 Feb 20, 2012 Cosyne practice (Evan, Jake, Memming, Mijung)
 Jan 30, 2012 NIPS highlights
2011
 Nov 30, 2011. Mijung & Memming (NIPS practice)
 Nov 16, 2011. Michael – Macke, Opper & Bethge, Common Input Explains HigherOrder Correlations and Entropy in a Simple Model of Neural Population Activity, PRL 2011.
 Nov 9, 2011. Round robin: Kenneth (latent variable models for LIP), Jacob (motion revcorr), Evan (voltage DR).
 Oct 26, 2011. Ozan – Ecker, Berens, Tolias, & Bethge, The effect of noise correlations in populations of diversely tuned neurons. J. Neurosci, 2011
 Oct 19, 2011. roundrobin
 Oct 12, 2011. Jonathan – Churchland et al, Variance as a Signature of Neural Computations during Decision Making, Neuron 2011.
 Oct 5, 2011. Memming – update on neural coding in LIP.
 Sept 28, 2011. (9:30am Wed): Kenneth – Butts et al, Temporal Precision in the Visual Pathway through the Interplay of Excitation and StimulusDriven Suppression, J Neurosci. 2011.
 Sept 19, 2011. Evan – summary of current work on models of intracellular voltage responses in V1, and Woods Hole MCN project.
 Sept 12, 2011. Joe Corey – summary of summer rotation project on GLMs and Ising models
 July 11, 2011(1pm): Mijung – Tipping: Sparse Bayesian Learning and Relevance Vector Machine. Journal of Machine Learning Research, (2001)
 July 18, 2011 (1pm): Jacob – Mineault, Barthelmé, & Pack: Bayesian methods for Noise Image Classification. Journal of Vision, (2009)
 July 11, 2011: Mijung – Mackay, Hyperparameters: Optimize, or integrate out? proceedings of the Thirteenth International Workshop on Maximum Entropy and Bayesian Methods, (1996). summary
 June 27, 2011 (1pm): Jonathan – tutorial on Kalman Filtering / Smoothing / EM and fast methods in matlab using sparse matrices. Background reading: A Unifying Review of Linear Gaussian Models, S. Rowies & Z. Ghahramani, Neural Computataion 1999.
 June 20, 2011: Jonathan – “A new look at statespace models for neural data”, Paninski et al, JCNS 2009
 April 18, 2011: Memming – Nemenman et al’s submillisecond information paper
 April 11, 2011: Mijung – computing optimal stimulus in the adaptive experimental design (at 1:30pm).
 April 04, 2011: Mijung – continued talking about adaptive experimental designs (from the updating rule for Covariance)
 Mar 30, 2011: Mijung – adaptive experimental designs
 Mar 23, 2011: Evan – sampling interpretation of neural activity
 Mar 14, 2011: Jonathan – recent developements on empirical Bayes
Advertisements