Organizers

• Eva Dyer  Northwestern University
• Allie Fletcher  Statistics, UCLA & UCB Redwood Center for Neuroscience
• Konrad Kording  Rehabilitation Inst of Chicago, Northwestern University
• Jascha Sohl-Dickstein  Google Research
• Joshua Vogelstein  Biomedical Engineering, Johns Hopkins University
• Jakob Macke caesar Bonn, an Institute of the Max Planck Society

Contributed Talks

• Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems

  Ingmar Kanitscheider*, Ila Fiete, University of Texas at Austin

• LFADS - Latent Factor Analysis via Dynamical Systems

  David Sussillo*, Google Brain

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

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