BigNeuro NIPS Workshop

BigNeuro Workshop @ NIPS

BigNeuro 2017

Datasets in neuroscience are increasing in size at alarming rates relative to our ability to analyze them. This workshop aims at discussing new frameworks for processing and making sense of large neural datasets. The morning session will focus on approaches for processing large neuroscience datasets. Examples include: distributed + high-performance computing, GPU and other hardware accelerations, spatial databases, and other compression schemes used for large neuroimaging datasets, online machine learning approaches for handling large data sizes, randomization and stochastic optimization. The afternoon session will focus on abstractions for modelling large neuroscience datasets. Examples include graphs, graphical models, manifolds, mixture models, latent variable models, spatial models, and factor learning.


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What is the format?

The morning will consist of two sessions focused on approaches for processing large neuroscience datasets. There will be three or four invited talks and one submitted talk per session, as well as time for an open panel discussion.

This format will be repeated in the afternoon, alongside a poster session and spotlight, as well as an open discussion at the end of the day.

Who can participate?

We welcome participants from the machine learning community, data scientists, and neuroscientists alike!

But I know nothing about neuroscience?!

If you don't know about neuroscience, don’t worry - we will introduce the basics behind some promising big neuro datasets and their associated computational challenges! This will set the stage for the afternoon talks and discussion, where we can talk about how machine learning can aid in analyzing these datasets.

When is BigNeuro?

The workshop is part of the NIPS workshop program, held at the Long Beach Convention Center, in Long Beach, CA. The event will take place from 8:30-5:30 on Saturday Dec 9th, 2017.

What is the basic schedule?

Both the morning and afternoon will be broken up into two sessions consisting of talks and a round-table discussion of the speakers. The afternoon will also consist of poster presentations.

Submission Deadline: Tuesday Oct. 31st, 2017

Submit your contribution for a talk or poster here!
The submission deadline is October 31st, 2017, and the decisions will be announced on November 10th, 2017. To be considered for a talk, please include a URL to a pre-print of an unpublished manuscript on arXiv (or related preprint service, such as biorRxiv). To be considered for a poster, please attach a PDF abstract of your proposed topic. Please attach only one of these documents in a single submission.


Welcome & Introductory Remarks

Time Presentation Location
8:45-9:00 Eva Dyer, Georgia Institute of Technology Room 204

Deep Learning & Neuro Session

Time Presentation Location
9:00-9:35 Jim DiCarlo, Massachusetts Institute of Technology,
“Can brain data be used to reverse engineer the algorithms of human perception?”
Room 204
9:35-9:55 Timothy Lillicrap, Google DeepMind,
“Backpropagation and deep learning in the brain”
Room 204
9:55-10:15 Viren Jain, Google,
“Algorithms, tools, and progress in connectomic reconstruction of neural circuits”
Room 204
10:15-10:35 Bing Brunton, University of Washington,
“Multimodal deep learning for natural human neural recordings and video”
Room 204
10:35-11:00 Coffee Break Room 204
11:00-11:35 Yoshua Bengio, University of Montreal,
"More Steps towards Biologically Plausible Backprop"
Room 204

Panel Discussion
Moderator: Will Gray Roncal

Time Presentation Location
11:40-12:20 "What neural systems can teach us about building better machine learning algorithms?"
Panel: Bing Brunton (U Washington), Jim DiCarlo (MIT), Timothy Lillicrap (DeepMind), Viren Jain (Google), Nathan Kutz (U Washington)
Room 204
12:20-13:40 Lunch Break Room 204

Dimensionality Reduction and Adaptive Experiments Session

Time Presentation Location
13:40-14:15 Nathan Kutz, University of Washington,
“Discovery of governing equations and biological principles from spatio-temporal time-series recordings”
Room 204
14:15-14:35 Eftychios Pnevmatikakis, Flatiron Institute, Simons Foundation,
“Large scale calcium imaging data analysis for the 99%”
Room 204
14:35-14:55 Gael Varoquaux, INRIA
“Machine learning for cognitive mapping”
Room 204

Contributed Talks / Poster Spotlights

Time Presentation Location
14:55-15:15 Pierre Bellec / Christian Dansereau, CRIUGM,
"Dealing with clinical heterogeneity in the discovery of new biomarkers of brain disorders"
Room 204
15:15-15:25 David Rolnick, MIT,
“Morphological error detection for connectomics”
Room 204
15:25-15:35 Poster Spotlights: Shariq Iqbal, Milad Makkie, Sang-Yun Oh Room 204

Poster Session

Time Presentation Location
15:45-16:30 Posters Room 204
15:15-15:25 Coffee Break Room 204

Macroscale Data/Functional Connectivity Session

Time Presentation Location
16:30-17:05 Bin Yu, UC Berkeley,
“Deep nets meet real neurons: pattern selectivity of V4 through transfer learning and stability analysis”
Room 204
17:05-17:25 Vince Calhoun, Mind Research Network
“Mapping brain structure and function with deep learning”
Room 204

Big Data Session
Moderator: Will Gray Roncal

Time Presentation Location
17:25-17:45 Nathan Drenkow, JHU Applied Physics Lab,
“bossDB: A Petascale Database for Large-Scale Neuroscience”
Room 204
17:45-18:05 Kristin Branson, Janelia Farm,
"Machine vision and learning for extracting a mechanistic understanding of neural computation"
Room 204
18:05-18:30 Discussion Room 204


  • Ming Bo Cai*, Stephanie S.Y. Chan, Yael Niv: Using a generative adversarial network to explain fMRI data

  • Guillem Cucurull*, Konrad Wagstyl, Arantxa Casanova, Petar Velickovic, Estrid Jakobsen, Adriana Romero, Alan Evans, Yoshua Bengio: Graph Convolutional Neural Networks for Cortical Mesh Segmentation
  • Brandon Duderstadt*, Jaewon Chung, Forrest Collman, Joshua Vogelstein: NOMADS: Neurodata’s Opensource Method for Automatic Detection of Synapses
  • Alex Fedorov, Eswar Damaraju, Vince Calhoun*, Sergey Plis: An (almost) instant brain atlas segmentation for large-scale studies
  • Andrea Giovannucci*, Johannes Friedrich, Matthew Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis: OnACID: Online analysis on calcium imaging data in real time
  • Shariq Iqbal*, John Pearson: A Goal-Based Movement Model for Continuous Multi-Agent Tasks
  • Christopher Kim*, Carson Chow: Learning arbitrary patterns in recurrent spiking networks
  • Milad Makkie*, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu: Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics
  • Matthias Minderer*, Kristen Brown, Christopher D. Harvey: Unsupervised mapping of visual-motor representations in mouse cortex during navigation
  • Daniel Moyer*, Greg ver Steeg, Joshua Faskowitz, Paul M. Thompson: How many tracts should we sample?
  • Rahul Nadkarni*, Nicholas J. Foti, Emily B. Fox: Learning Dynamic Functional Connectivity Networks from Infant Magnetoencephalography Data
  • Sang-Yun Oh*, Alnur Ali*, Penporn Koanantakool, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Leonid Oliker, Katherine Yelick: Whole-brain Functional Connectivity Mapping and Region Segmentation from Distributed Estimation of Voxel-level Sparse Precision Matrix
  • Ravi Tejwani*, Adam Liska, Hongyuan You, Jenna Reinen, Payel Das: Autism Classification Using Brain Functional Connectivity Dynamics and Machine Learning
  • Andrew Warrington*, Frank Wood: Updating the VESICLE-CNN Synapse Detector
  • Hongyuan You*, Adam Liska, Vinay Shahidhar, Payel Das: The Underlying Brain Functional Landscape of an Individual as revealed by Graph Embedding
  • Michael Shvartsman*, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore Wilke, Jonathan D. Cohen: Matrix-normal models for fMRI analysis