Program
07.30 The organizers: Opening remarks
07.45 Terry Sejnowski (Salk Institute): Keynote
Session 1: Recording neural activity with light
08.30 Andreas Tolias (Baylor College of Medicine)
09.00-09.30 Break 1 & Posters
09.30 Misha Ahrens (Janelia Farm/HHMI): Whole-brain functional imaging and motor learning in the larval zebrafish
Session 2: Probabilistic models of neural activity
10.00 Jonathan Pillow (University of Texas, Austin): Flexible models for binary spike patterns in large-scale neural recordings
10.30 David Greenberg (MPI Tübingen and CAESAR Bonn)
11.00 Discussion
11.30-15.00 Ski break
Session 3: Other large-scale recording methods
15.00 Jorg Scholvin (MIT): High-density electrode arrays
15.30 Konrad Kording (Northwestern University)
16.00 Jakob Macke (MPI & BCCN Tübingen)
16.30 Discussion
16.45-17.15 Break 2 & Posters
Session 4: Neural connectivity
17.15 Joshua Vogelstein (Duke University): Statistical Models and Inference for Big Brain-Graphs
17.45 Mitya Chklovskii (Janelia Farm/HHMI):
18.15 Final discussion: What will we learn with all this new data? Dream experiments?
Posters
Ari Pakman, Ben Shababo and Liam Paninski, Bayesian Probit Factor Models with Sparse Latent Variables For Neural Population Spiking Data.
Evan Archer, Jonathan Pillow and Jakob H. Macke, Low-dimensional models of neural population recordings with complex stimulus selectivity.
David Pfau, Jeremy Freeman, Misha Ahrens and Liam Paninski, Scalable ROI Detection for Calcium Imaging.
Jeremy Freeman, Nikita Vladimirov, Takashi Kawashima and Misha Ahrens, Mapping the brain at scale.
Christopher Hillar and Urs Köster, Characterizing high-dimensional neural recordings with Hopfield networks.
Urs Köster and Bruno Olshausen, Testing V1 receptive field models with polytrode recordings.
Scott W. Linderman and Ryan P. Adams, Fully-Bayesian Inference of Structured Functional Networks in GLMs.
Ferran Diego, Susanne Reichinnek, Martin Both and Fred A. Hamprecht, Automated Identification of Neuronal Activity From Calcium Imaging By Sparse Dictionary Learning.