<3meta http-equiv="X-UA-Compatible" content="IE=edge"> NIPS 2016 Workshop: Brains and Bits: Neuroscience Meets Machine Learning
SCHEDULE : (Workshop description here )
Location: Room 211 CCIB, Barcelona Convention Center, Barcelona, Spain; 9 and 10 December
Time Day 1
8:30 Opening remarks and welcome: Allie Fletcher & Eva Dyer
Normative Models of Neural Computation
8:45 Christos Papadimitriou   A Computer Scientist Thinks About the Brain*     *keynote
9:30 Cristina Savin   Spike-based Probabilistic Computation
10:00 Mitya Chklovskii   Toward Biologically Plausible Machine Learning: A Similarity Matching Approach
10:30 Coffee Break 1a (+ Posters)
Statistical Models for Neural Data Analysis
11:00 Jonathan Pillow   Scalable Inference for Structured Hierarchical Receptive Field Models
11:30 Emily Fox   Functional Connectivity in MEG via Graphical Models of Time Series
12:00 Srini Turaga   Independence testing & Amortized inference, with neural networks, for neuroscience
12:30-14:00 Lunch + Posters
Analyzing Neural Population Dynamics
14:00 Il Memming Park   Dynamical Systems Interpretation of Neural Trajectories
14:30 David Sussillo   LFADS - Latent Factor Analysis via Dynamical Systems
15:00 Coffee Break1b (+ Posters)
15:30 Poster Session 1b
Biological Networks
16:00 Eva Dyer  
16:30 Michael Buice   The Allen Brain Observatory
17:00 Stefan Mihalas   Modeling Optimal Context Integration in Cortical Columns
17:30 Breakout Discussion   Konrad Kording and Eva Dyer
Time Day 2
8:30 Opening remarks Day 2: Jascha Sohl-Dickstein & Eva Dyer
Deep Learning and the Brain
8:45 Yoshua Bengio   Toward Biologically Plausible Deep Learning*     *keynote
9:30 Surya Ganguli   Deep Neural Models of the Retinal Response to Natural Stimuli
10:00 Max Welling   Making Deep Learning Efficient Through Sparsification
10:30 Coffee Break + Posters
11:00 David Cox   Predictive Coding for Unsupervised Feature Learning
11:30-12:30 Panel: From Brains to Bits and Back Again
Panelists: Yoshua Bengio, Demis Hassabis, Christos Papadimitriou, & Macke, Savin, Sejnowski, Tolias
Moderators: Konrad & Allie
12:30-14:00 Lunch Break + Posters 
Analysis of Neuroimages
14:00 Fred Hamprecht   Motif Discovery in Functional Brain Data
14:30 Anima Anandkumar   TBA
15:00 Coffee Break2b (+ Posters)
15:30 Poster Session 2
Normative Models of Neural Computation II
16:00 Ingmar Kanitscheider   Training Recurrent Networks to Generate Hypotheses About How the Brain Solves Hard Navigation Problems
16:30 Jörg Lücke   Probabilistic Inference and the Brain: Towards General, Scalable, and Deep Approximations
17:00 Wrap up

Organizers



Contributed Talks

Accepted Posters

  • Circuits in the retina: Deep learning as a biological modeling tool
    Dawna Bagherian*, Taehwan Kim, Yisong Yue, Markus Meister
  • Multilayer recurrent network models of primate retinal ganglion cells
    Eleanor Batty*, Nora Brackbill, Josh Merel, Alexander Heitman, Alexander Sher, Alan Litke, E.J. Chichilnisky, Liam Paninski
  • Estimating Mutual Information from Average Classification Error
    Yuval Benjamini*, Charles Zheng
  • Efficient Randomized Filtering for Dimensionality Reduction in Electrophysiology Data
    Nicholas Bertrand*, Han Lun Yap, Adam Charles, Christopher Rozell
  • Modelling human probabilistic categorization with neural reinforcement learning
    Sander Bosch*, Katja Seeliger, Marcel van Gerven
  • Short-term Sequence Memory in Recurrent Networks
    Adam Charles*, Han Lun Yap, Dong Yin, Christopher Rozell
  • Unsupervised learning of manifold models for neural coding of physical transformations in the ventral visual pathway
    Marissa Connor*, Christopher Rozell
  • Multi-view techniques for the joint electrophysiological and morphological classification of neuronal cell types
    Ellese Cotterill*, Stephen Eglen
  • Predicting the consequence of action in digital control state spaces
    Emmanuel Daucé*
  • Computational Geometry for Population Density Techniques: a Bridge Between Individual Neurons and Imaging Techniques?
    Marc De Kamps*, Yi Ming Lai
  • Efficient inference for temporal dependencies and low-rank structure in spatiotemporal receptive fields
    Lea Duncker*, Sneha Ravi, Greg Field, Jonathan Pillow
  • Interplay between off-line replay and plasticity in memory consolidation
    Nathalie Dupuy*, Mark van Rossum
  • Discovery of Salient Low-Dimensional Dynamical Structure in Neuronal Population Activity Using Hopfield Networks
    Felix Effenberger*, Christopher Hillar
  • Neural Simpletrons – Minimalistic Directed Generative Networks for Learning with Few Labels
    Dennis Forster*, Abdul-Saboor Sheikh, Joerg Luecke
  • Analysis of Motor Imagery Signals by Recurrent Neural Network
    Hiroyasu Fukutomi, Takeshi Higuchi, Hideo Mukai*
  • Modern machine learning tools for neural decoding of movement
    Joshua Glaser*, Konrad Kording
  • Large-Scale Noisy-OR Networks as Models for Inference and Learning in Neurosensory Systems
    Enrico Guiraud*, Joerg Bornschein, Joerg Luecke
  • A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control
    Ivan Herreros-Alonso*, Xerxes Arsiwalla, Paul Verschure
  • Automatic cell identification in hard X-ray tomograms of human brain tissue
    Simone Hieber*
  • Implementation of Faster P300 EEG Spelling System by Online Learning
    Takeshi Higuchi, Hiroyasu Fukutomi, Hideo Mukai*
  • Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data
    Devon Hjelm*, Vince Calhoun-Jr, Sergey Plis
  • Neuromodulator-based learning in biological and artificial neural networks
    Raphael Holca-Lamarre*, Klaus Obermayer, Joerg Luecke
  • Ensembles in medial and lateral orbitofrontal cortex construct cognitive maps emphasizing different features of the behavioral landscape
    Nina Lopatina*, Brian Sadacca, Geoffrey Schoenbaum, Michael McDannald
  • Predictive Coding Inspired Networks for Unsupervised Learning and Next Frame Prediction
    William Lotter*
  • Anticipatory actions: anticipated motor commands or reactions to sensory predictions?
    Giovanni Maffei*, Ivan Herreros, Marti Sanchez-Fibla, Paul Verschure
  • Learning from the interaction: Closed-loop optimization of neural modulation using interactive machine learning
    Babak Mahmoudi*
  • Adapting magnetic interactions: from data storage towards hardware that learns
    Johan Mentink*, Bert Kappen, Misha Katsnelson, Alex Khajetoorians, Theo Rasing
  • Temporal Information Demultiplexing: A Novel Approach to the Analysis of Multiplexed Neural Codes
    Manuel Molano Mazon*, Arno Onken, Houman Safaai, Stefano Panzeri
  • Deep Learning Captures V2 Selectivity for Natural Textures
    Md Nasir Uddin Laskar, Luis G Sanchez Giraldo, Odelia Schwartz*
  • Using EEG Covariance Matrices and Riemannian Geometry for Detecting Respiratory Discomfort in Humans
    Xavier Navarro-Sune*, Anna Hudson, Fabrizio De Vico Fallani, Jacques Martinerie, Adrien Witon, Pierre Pouget, Mathieu Raux, Thomas Similowski, Mario Chavez
  • The effect of noise correlations on information coding by neuronal ensembles during perceptual decision-making
    Ramon Nogueira*
  • An olfactory system model finds sparse solutions to the linear inverse problem using the L2 norm
    Gonzalo Otazu*, Paul Masset, Dinu Albeanu
  • Bio-inspired unsupervised pre-training of convolutional neural networks for image classification
    Geoffrey Portelli*, Mélanie Ducoffe, Frédéric Lavigne, Frédéric Precioso
  • Generalization bound for kernel similarity learning
    Michael Rabadi*
  • Inter-layer relationship between CNNs and the human brain
    Kandan Ramakrishnan, Rajat Mani Thomas*
  • Understanding biological computation through synthetic neurophysiology
    Pavan Ramkumar*, Hugo Fernandes, Matthew Smith, Konrad Kording
  • A rich source of labels for deep network models of the primate dorsal visual stream
    Omid Rezai*, Pinar Boyraz Jentsch, Bryan Tripp
  • Recurrent computations for pattern completion
    Martin Schrimpf*
  • Decoding and encoding retinal ganglion cell responses with deep neural networks
    David Schwab*, Stephanie Palmer, Thierry Mora, Olivier Marre
  • Reward-based training of recurrent neural networks for cognitive and value-based tasks
    Francis Song*, Guangyu Robert Yang, Xiao-Jing Wang
  • Towards Local Learning and MCMC Inference in Biologically Plausible Deep Generative Networks
    Jost Tobias Springenberg*, Katharina Wilmes, Joschka Boedecker
  • Combining Multiscale Diffusion Kernels for Learning the Structural and Functional Brain Connectivity
    Sriniwas Surampudi*, Shruti Naik, Avinash Sharma, Bapi Raju Surampudi, Dipanjan Roy
  • Towards end-to-end optimisation of functional image analysis pipelines
    Albert Vilamala*, Kristoffer Hougaard Madsen, Lars Kai Hansen
  • Unsupervised Learning of Spatio-Temporal Features from Retinal Neuronal Signals
    Riccardo Volpi*
  • Canonical Polyadic Tensor Decomposition Identifies Inputs to Artificial Networks
    Alex Williams*
  • Deep Learning in Multi-Layer Architectures of Dense Nuclei
    Yonghua Yin*, Erol Gelenbe
  • CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
    Zhaofei Yu, David Kappel*, Robert Legenstein, Sen Song, Feng Chen, Wolfgang Maass
  • A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
    Hejia Zhang*, Po-Hsuan (Cameron) Chen, Janice Chen, Xia Zhu, Javier Turek, Ted Willke, Uri Hasson, Peter Ramadge
  • Predicting psychotic symptoms in 22q11.2 deletion syndrome based on resting-state BOLD variability features extracted by PLS correlation.
    Daniela Zöller*, Marie Schaer, Elisa Scariati, Maria Carmela Padula, Stephan Eliez, Dimitri Van De Ville