Lab meetings

Current meeting time: Tuesdays, 12:30-2:00pm

Current Schedule

• Mar 28 – Ljubica Cimesa & Othman Lahrach (ENS visitors)
• Apr 4 – Dean Pospisil
• Apr 11 – Matt Creamer
• Apr 18 – Yoel Sanchez-Araujo
• Apr 25 – Iris Stone
• May 2 – no meeting (SCGB meeting in NYC)
• May 9 – no meeting (PNI retreat)
• May 16 – Zeinab Mohammadi
• May 23 – Kevin Chen
• May 30 – Bichan Wu

Past Meetings

2023

• Mar 21 – post-cosyne wrap up
• Mar 14 – no meeting (Cosyne)
• Mar 7 – Alex Riordan
• Feb 28 – COSYNE poster/talk practice
• Feb 21 (12pm) – Taiga Abe (visiting speaker)
• Feb 14 – Victor Geadah – Diffusion Modeling (part deux) –Denoising Diffusion Probabilistic Models and Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser
• Feb 7 – Aditi Jha – Organizing recurrent network dynamics by task-computation to enable continual learning
• Jan 31 – Ling-Qi Zhang (visiting speaker)
• Jan 24 – Nadav Amir: research talk
• Jan 17 – Anuththara Rupasinghe – Neural Network Poisson Models for Behavioural and Neural Spike Train Data
• Jan 10 – round robin

2022

• Dec 20 – Srdjan Ostojic
• Dec 14 (Wed) – visiting speaker (Chanmin Yu).
• Dec 13 – Adrian Valente (visitor from Srdjan’s group)
• Dec 6 – Code Review discussion
• Nov 29 – Victor Geadah – Diffusion models tutorial. Reading: Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015). Sohl-Dickstein, Weiss, Maheswaranathan, & Ganguli.
• Nov 22 – Kevin Chen – Paper: Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
• Nov 15 – no meeting (SFN)
• Nov 1 – Iris Stone – Paper: Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations
• Oct 25 – Lea Duncker ( 3pm ); Lunch only (no presentation) at 12:30
• Oct 18 – No meeting, lunch only.
• Oct 11 – Brian DePasquale – Paper: Neural population dynamics in dorsal premotor cortex underlying a reach decision
• Oct 4 – Dean Pospisil – Paper: Meta-learning for Mixed Linear Regression. 
• Sept 27 – Zeinab Mohammadi – Paper: YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina
• Sept 20 – Ben Cowley – Paper: Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
• Sept 13 – Yoel Sanchez-Araujo – Julia tutorial (notebook)
• Sept 6 – Matt Creamer – Paper: A quantitative model of conserved macroscopic dynamics predicts future motor commands
• August pause
• July 26 – Pati Stan – Research talk: The effects of stimulus expectation on perception and V4 single neuron and population activity.
• July 19 – Zoe Ashwood: practice talk
• July 6 (Weds) – Orren Karniol-Tambour: research talk
• June 28 – Alex Riordan – tutorial on Ca imaging.
• Jun 21 – Rich Pang – research talk: Path vectors: a neural code for sequential memory
• Jun 14 – Amy Christensen (visiting speaker) – “Rats use uncertainty to guide learning and behavior”
• Jun 7 – Brian DePasquale – Research talk on the training and interpretation of spiking neural networks. (Moderator: Orren Karniol-Tambour)
• May 24 – Dean Pospisil (Moderator: Brian DePasquale) – Estimating Learnability in the Sublinear Data Regime.
• May 10 – David Zoltowski – Practice Talk.
• Apr 26 – Daniel Greenidge (Moderator: Dean Pospisil) – Practice talk.
• Apr 19 – Cosyne meeting review (Moderator: Aditi Jha)
• Apr 5 – Zeinab Mohammadi (Moderator: Matt Creamer) – Tracking human skill learning with a hierarchical Bayesian sequence model.
• Mar 29 – Zoe Ashwood (Moderator: Ben Cowley) – Practice talk.
• Mar 15 – Yoel Sanchez Araujo – Research talk (Moderator: David Zoltowski) – In PNI 130
• Mar 8 – Aditi Jha (Moderator: Orren Karniol-Tambour)
• Mar 1 – Ben Cowley (Moderator: Zoe Ashwood) – Unsupervised neural network models of the ventral visual stream.
• Feb 22 – Daniel Greenidge (Moderator: Yoel Sanchez Araujo) – Selected chapters from Causal Inference: What If?
• Feb 15 – Matt Creamer (Moderator: Iris Stone) – Research talk.
• Feb 8 – Visiting speaker – Anuththara Rupasinghe
• Feb 1 – Research update talks from Dean, Aditi & Kevin (Moderator: David Zoltowski)
• Jan 25 – David Zoltowski (Moderator: Rich Pang) – Research talk.
• Jan 18 – Paper round robin
• Jan 11 – Kevin Chen – Research Talk. (Moderator: Zeinab Mohammadi)

2021

• Dec 14 – Daniel Greenidge (Moderator: Zoe Ashwood) – Selected chapters from Causal Inference: What If?
• Dec 7 – Yoel Sanchez Araujo – Practice talk. (Moderator: Aditi Jha)
• Nov 30 – David Zoltowski – Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders. (Moderator: Daniel Greenidge)
• Nov 23 – Yoel Sanchez Araujo – Research Talk. (Moderator: David Zoltowski)
• Nov 16 – Zeinab Mohammadi – Targeted Neural Dynamical Modeling (Moderator: Dean Posipisl)
• Nov 9 – Ben Cowley – Generalized Shape Metrics on Neural Representations (Moderator: Matt Creamer)
• Nov 2 – Cosyne Workshop.
• Oct 26 – Rich Pang – Research Talk (Moderator: Orren Karniol-Tambour)
• Oct 19 – Aditi Jha – Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design (Moderator: Yoel Sanchez Araujo)
• Oct 12 – Matt Creamer – Research Talk (Moderator: Brian DePasquale)
• Oct 5 – Dean Pospisil – Research talk (Moderator: Zeinab Mohammadi)
• Sep 28 – Round robin.
• Sep 20 – Alexander Riordan (research talk)
• July 26 – Daniel Greenidge. The Blessings of Multiple Causes. Yixin Wang & David Blei. JASA 2020.
• July 19 – Zoe Ashwood – Deep Reinforcement Learning Tutorial (Moderator: Rich Pang)
• July 12 – David Zoltowski – Evaluating the Interpretability of Generative Models by Interactive Reconstruction (Moderator: Iris Stone)
• Jun 28 – Orren Karniol-Tambour – Geometry of abstract learned knowledge in the hippocampus (Moderator: Aditi Jha)
• Jun 21 – Brian DePasquale – Practice talk (Moderator: David Zoltowski)
• Jun 14 – Yoel Sanchez Araujo – Mesolimbic dopamine adapts the rate of learning from errors in performance (Moderator: Zoe Ashwood)
• Jun 7 – Zeinab Mohammadi – Dopaminergic and Prefrontal Basis of Learning from Sensory Confidence and Reward Value (Moderator: Iris Stone)
• June 1 – Iris Stone – Efficient and stochastic mouse action switching during probabilistic decision making (Moderator: Orren Karniol-Tambour)
• May 17 – Brian DePasquale – Representation learning for neural population activity with Neural Data Transformers (Moderator: Matt Creamer)
• May 10 – Aditi Jha – practice talk (Moderator: Abby Russo)
• May 3 – Ben Cowley – Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations, Dapello et al. (Moderator: Mike Morais) –
• Apr 26 – Mike Morais – Practice FPO talk (Moderator: Brian DePasquale)
• Apr 19 – Rich Pang – Rotational dynamics reduce interference between sensory and memory representations, Libby & Buschman (Moderator: Aditi Jha)
• Apr 12 – Liz Spencer (visiting speaker)
• Mar 29 – Zoe Ashwood – Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. Ritter, Barrett, Santoro, & Botvinick. (Moderator: Daniel Greenidge)
• Mar 22: Matt Creamer – The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging. Warrington, Spencer, Wood. 2019.
• Mar 16: Daniel Greenidge – Similarity of Neural Network Representations Revisited. Kornblith, Norouzi, Lee, Hinton. ICML 2019.
• Mar 9: Dean Pospisil (visiting speaker)
• Mar 2: Round robin
• Feb 9: David Zoltowski: practice talk
• Jan 19: Orren Karniol-Tambour: practice talk

2020

• Dec 15: Rich Pang – research talk: Mechanisms of sequence production and processing inspired by singing animals.
• Dec 8: Yoel Sanchez Araujo – Monte Carlo Gradient Estimation in Machine Learning. Mohamed, Rosca, Figurnov, & Mnih (2020)
• Dec 1: Abby Russo – practice talk: “Population activity geometry as a bridge between computation and neural implementation”
• Nov 24: Zoe Ashwood – Normalizing Flows for Probabilistic Modeling and Inference. Papamakarios, Nalisnick, Rezende, Mohamed, & Lakshminarayanan (2019)
• Nov 17: Anqi Wu: practice talk
• Nov 10: Orren Karniol-Tambour – Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. Grathwohl et al 2019.
• Nov 3: David Zoltowski – Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. Glaser, Whiteway, Cunningham, Paninski, & Linderman. NeurIPS 2020.
• Oct 27: Jonathan Pillow – Tutorial on Variational Autoencoders. Carl Doersch. arxiv 2016
• Oct 20: Mike Morais – Learning probabilistic neural representations with randomly connected circuits. Maoz, Tkacik, Esteki, Kiani, & Schneidman. PNAS (2020).
• Oct 13: Brian DePasquale – Considerations in using recurrent neural networks to probe neural dynamics. Kao, J. Neurophysiol (2019)
• Sept 22: Iris Stone – practice talk
• Sept 15: Matt Creamer – Inferring single-trial neural population dynamics using sequential auto-encoders. Pandarinath et al, Nature Methods (2018).
• Sept 8: round robin
  July 30: Kevin Chen – Convergent Temperature Representations in Artificial and Biological Neural Networks. Haesemeyer, Schier, & Engert (2019)
• July 24  (Friday): Aditi Jha –  Bayesian Deep Learning and a Probabilistic Perspective of Generalization. Wilson & Izmailov (2020).
• July 2: Ben Cowley: A disciplined approach to neural network hyper-parameters. 
  Leslie Smith (2018).
• June 18: Nick Roy – FPO practice talk
• Jun 11: Abby Russo: Learning is shaped by abrupt changes in neural engagement. Hennig, Oby, Golub, Bahureksa, Sadtler, Quick, Ryu, Tyler-Kabara, Batista, Chase, & Yu 2020.
• May 28: Iris Stone: “The striatal contribution to behavior depends on cognitive state” (research talk).
• May 14: Zeinab Mohammadi (external speaker).
• May 7: Stephen Keeley: Cortical areas interact through a communication subspace. Semedo, Zandvakili, Machens, Byron, & Kohn.  Neuron 2019.
• Apr 30: Ben Cowley – research talk.
• Apr 23: Adam Charles – A zero-inflated gamma model for post-deconvolved calcium imaging traces. Wei, Zhou, Grosmark, Ajabi, Sparks, Zhou, Brandon, Losonczy &  Paninski 2019.
• Apr 16: Mikio Aoi – Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics. Lebedev, Ossadtchi, Mill, Urpí, Cervera & Nicolelis (2019).
• Apr 9: David Zoltowski: Machine translation of cortical activity to text with an encoder–decoder framework. Makin, Moses & Chang (2020).
• Apr 2: Daniel Greenidge: Euler’s Formula and the Eigenvectors of Circulant Matrices (tutorial).
• Mar 26: Mike Morais: Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. Havasi, Peharz, & Hernández-Lobato (2018).
• Mar 19: Nick Roy: One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. Morcos, Yu, Paganini, & Tian (2019).
  (related FAIR blog post).
• Mar 12: research talk: Matt Creamer: “Prediction of future calcium dynamics in C. elegans”
• Mar 5: Cosyne highlights
• Feb 27: Cosyne (no meeting)
• Feb 20: Zoe Ashwood – practice talk: “Discrete latent states underlie sensory decision making behavior.”
• Feb 13: Mikio Aoi – practice talk.
• Feb 7 (Fri): round-robin

2019

• Dec 12: Kevin Chen: Robust computation with rhythmic spike patterns, Frady & Somer 2019. 
• Dec 5: Daniel Greenidge: tutorial on Metropolis Hastings and MCMC
• Nov 14: Mike Morais – research talk: “Loss-calibrated expectation propagation for decision-robust approximate inference.”
• Nov 21: Brian DePasquale –  reproducible coding / github / unit testing / travis 
• Nov 7: Ben Cowley – Attention is all you need. (Transformers paper!)  Vaswani et al, NeurIPS (2017). [summary]
• Oct 25: Marcus Triplett: “Neural encoding models for multivariate optical imaging data.”
• Oct 24: Brain COGS retreat
• Oct 10: Abby Russo – Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation. Russo et al (2019)
• Oct 3: Brian DePasquale – Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons,
  Sederberg & Nemenman (2019)
• Sept 26: Zoe Ashwood – generals practice talk (“Characterizing Animal Decision-Making Strategies with Hidden Markov Models and Generalized Linear Models”)
• Sept 19: David Zoltowski: generals practice talk
• Aug 2 (Friday): Stephen Keeley – The continuous Bernoulli: fixing a pervasive error in variational autoencoders. Loaiza-Ganem & Cunningham (2019).
• July 24: David Zoltowski: research talk (“Recurrent state-space models constrained by decision making theory”).
• July 17: Mike Morais: practice talk (“”Extending efficient coding to more diverse families of optimal codes in Bayesian observer models”)
• July 10: Adam Charles: Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models.Dai, Wang, Aston, Hua, & Wipf. JMLR (2018).
• July 3: Anqi Wu: Deep learning with differential Gaussian process flows. Hegde, Heinonen, Lähdesmäki, & Kaski. PMLR (2019).
• June 27 @ noon (Thursday): Nick Roy – RL mini-bootcamp (Sutton & Barto Chap 1-8)
• June 19: Daniel Greenidge – Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Li, Jamieson, DeSalvo, Rostamizadeh, & Talwalkar. JMLR (2018).
• June 5: Zoe Ashwood – Lapses in perceptual judgments reflect exploration. Pisupati, Chartarifsky-Lynn, Khanal, & Churchland. biorxiv 2019. [summary]
• May 29: Ben Cowley – The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. Frankle & Carbin, ICLR 2019. [summary]
• May 8 (11:30am): visiting speaker: Dominika Lyzwa
• May 1: Kevin Chen – Soft Q-Learning with Mutual-Information Regularization. 
  Grau-Moya, Leibfried, & Vrancx.  ICLR 2019.
• Apr 24: Brian DePasquale – Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.  Lee, Xiao, Schoenholz, Bahri, Sohl-Dickstein, & Pennington 2019.
• Apr 17: Mikio Aoi – Inferring the function performed by a recurrent neural network. Chalk, Tkacik, & Marre 2019.
• Apr 3 (special mtg): Daniel Greenidge – Scalable GP-Poisson regression with SGD variational inference. (ref: Doubly Stochastic Variational Inference for Deep Gaussian Processes, Salimbeni & Deisenroth 2017).
• Apr 3: Zoe Ashwood – Deep Neural Networks as Gaussian Processes. Lee, Bahri, Novak, Schoenholz, Pennington, & Sohl-Dickstein 2018. [summary]
• Mar 26 (Tues @ 11:15): Noga Mosheif – “Efficiency and stability of the modular grid cell code”
• Mar 20: Adam Charles (practice talk)
• Mar 13: Cosyne highlights
• Feb 20: David Zoltowski – oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis. Ainsworth, Foti, Lee, & Fox. ICML 2018. [summary]
• Feb 13: Stephen Keeley – Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. Grover & Ermon 2018. [summary]
• Feb 6: Mike Morais – beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (pdf). Higgins, Matthey, Pal, Burgess, Glorot, Botvinick, Mohamed, & Lerchner, ICLR 2017.
• Jan 23: Nick Roy – Machine Theory of Mind. Rabinowitz, Perbet, Song, Zhang, Eslami, & Botvinick. [summary]
• Jan 16: Ben Cowley: Insights on representational similarity in neural networks with canonical correlation. Morcos, Raghu, & Bengio, NeurIPS 2018. [summary]

2018

• Dec 14: NeurIPS highlights
• Dec 7: no meeting (NeurIPS)
• Nov 30: Anqi Wu – Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. Wu, Nowozin, Meeds, Turner, Hernandez-Lobato, & Gaunt, 2018.
• Nov 23: no meeting (Thanksgiving)
• Nov 16: round robin
• Oct 26: Ben & Mikio (SFN practice talks)
• Oct 19: BRAIN COGS retreat
• Oct 12: Farhan Damani – Approximate inference for the loss-calibrated Bayesian.  Lacoste–Julien, Huszár & Ghahramani, aistats 2011. [summary].
• Oct 5: Brian DePasquale – An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability.  Wei, Li, Svoboda & Druckmann 2018
• Sept 21: Mike Morais (generals talk)
• 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: research talk
• 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

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 (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)

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: 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

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 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].

Spring-Summer 2015:

• 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.

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: 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)

2013

• 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).

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 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

2011

• 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