Current meeting time: Fridays, 12:00-1:30pm (PNI 511).
- Sept 21: Mike Morais (generals talk)
- Sept 28: no meeting.
- Oct 5: Brian DePasquale – An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability. Wei, Li, Svoboda & Druckmann 2018.
- Oct 12: Farhan Damani
- Oct 19: BRAIN COGS retreat
- Oct 26: Ben & Mikio (SFN practice talks).
- Sept 12: round robin
- Aug 29: Adam Charles – research talk on SEUDO
- Aug 22: David Zoltowski: research talk on paGLM
- July 25: Hugo Richard: research talk
- July 11-18: no meeting (CSHL vision course)
- June 27: Stephen Keeley
- June 20: Mike Morais: research talk
- June 13: Nick Roy: research talk
- May 30: no meeting (Jonathan speaking at Rutgers Comp Neuro Symposium)
- May 24: no meeting (Nick & Jonathan at IBL meeting in Paris).
- May 9: Anqi Wu: practice for retreat talk (Poisson-GPLV for latent embedding of spike trains).
- May 2: Camille Buxo – Learning arbitrary dynamics in efficient, balanced spiking networks using local plasticity rules. Alemi, Machens, Denève, & Slotine 2017.
- April 25: Matt Whiteway (visiting speaker)
- April 18: Anqi Wu – Doubly Stochastic Variational Inference for Deep Gaussian Processes. Salimbeni & Deisenroth 2017.
- April 13 (Fri @ 10am): Adam Charles & Mikio Aoi – A cerebellar mechanism for learning prior distributions of time intervals. Narain, Remington, De Zeeuw & Jazayeri 2018. (joint meeting with Wang lab).
- April 4: Jonathan Pillow – Gaussian processes for Big Data. Hensman, Fusi, & Lawrence 2013.
- Mar 14: cosyne review
- Feb 28: cosyne poster practice
- Feb 21: Brian DePasquale – Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response Russo, Bittner, Perkins, Seely, London, Lara, Miri, Marshall, Kohn, Jessell, Abbott, Cunningham, & Churchland. Neuron 2018. [summary]
- Feb 14: Farhan Damani – Fast epsilon-free 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
- 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 (11am-12pm in room 101)
- Oct 18: Anqi Wu – Learning Scalable Deep Kernels with Recurrent Structure. Al-Shedivat, Wilson, Saatchi, Hu, & Xing; JMLR 2017. [summary]
- Oct 11: Jonathan – PASS-GLM: 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 re-analysis of the motion-pulse 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 Black-box 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 – Stimulus-Driven 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 Non-Conjugate 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 Gumbel-Softmax. 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 (KISS-GP), 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- “Low-dimensional, dynamic encoding in prefrontal cortex during decision-making” (practice talk)
- 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: Human-level control through deep reinforcement learning. Mnih et al, Nature (2015).
- Oct 5: David: “On the role of time in perceptual decision-making.” 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 Non-spatial State Representations in Humans. Kurth-Nelson, 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 conductance-based 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 Sub-Second 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 t-SNE. Van der Maaten & Hinton. JMLR (2008)
- Mar 23: Ji Hyun – Learning In Spike Trains: Estimating Within-Session 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: Nuclear-norm-penalised multivariate time-series autoregression. (Note special time: 11a-12p).
- Jan 12: Anqi – Auto-encoding variational bayes. Kingma & Welling, arxiv 2013
- 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 over-dispersed 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].
- July 27: Mikio – A category-free neural population supports evolving demands during decision-making. 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 MT-LIP modeling & analysis
- May 19: Angela – internal talk on decision-making
- 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 spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. Brunton, Johnson, Ojemann, & Kutz, arxiv 2014. [summary]
- Mar 16: Cosyne review.
- 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: 1D-dynamics 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 neuro-computational model of area LIP.”
Ziesche and Hamker, Front Comp Neurosci 2014.
- June 23: Kate – “Explaining human multiple object tracking as resource-constrained 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 – non-parametric 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: Context-dependent 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)
- Dec 17 – NIPS wrapup
- Dec 3 – NIPS practice poster presentations
- Nov 26 – Kate: Context-dependent 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 Translation-Invariant 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: Hessian-free optimization (Martens 2010). [summary].
- June 3 – round-robin 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).
- 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 state-space models and online algorithms
- Aug 13, 2012. Mijung – Discussion on over-dispersed Poisson-GP 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 log-likelihood trick for generalized quadratic models.
- June 11, 2012. Jonathan – digression on “single-spike 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
- Nov 30, 2011. Mijung & Memming (NIPS practice)
- Nov 16, 2011. Michael – Macke, Opper & Bethge, Common Input Explains Higher-Order 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. round-robin
- 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 Stimulus-Driven 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 state-space 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