High-Dimensional Statistical Inference in the Brain

Schedule

Time
7:30 AM	Poster set-up and opening
7:45	Jonathan Pillow: Local and low-rank priors for efficient high-dimensional receptive field estimation
8:20	Eftychios Pnevmatikakis: Extracting information from calcium imaging data
8:45	Coffee
9:00	Wolfgang Maass: Distributions of high-dimensional network states as knowledge base for networks of spiking neurons in the brain
9:35	Terry Sejnowski: TBA
10:10	Surya Ganguli: A theory of neural dimensionality, dynamics and measurement: the neuroscientist and the single neuron
10:45	Poster session, lunch, and ski
3:30 PM	Maneesh Sahani: Dimensionality, dynamics and (de)synchronisation in the auditory cortex
4:05	Matthias Bethge: Normative models and identification of nonlinear neural representations
4:40	Ian Stevenson: What can slice physiology tell us about inferring functional connectivity from spikes?
5:15	Coffee
5:30	Ryan Adams: Implementing graphical models with chemical reaction networks
6:05	Mitya Chklovskii: How the brain handles big data: Online algorithms in neurons
6:40	Wrap up

S. Zayd Enam and Michael R. DeWeese: Time-frequency inseparable receptive field models. [Paper]
T. Furlanello, M. Cristoforetti, C. Furlanello and G. Jurman: Sparse predictive structure of deconvolved functional brain networks [Poster] [Paper]
Matthew Lawlor and Steven W. Zucker: Tensor decomposition by modified BCM neurons finds mixture means through input triplets. [Paper]
Mayur Mudigonda, Nicolas Muller, Aditya Joshi, Christopher Hillar, and Friedrich Sommer: Learning non-local features for classification using compressed sensing and sparse coding
Michael D. Oliver and Dustin E. Stansbury: Learning non-linear receptive field models of V2 neurons using deep networks
Jacquelyn A. Shelton, Abdul-Saboor Sheikh, Philip Sterne, Jorg Bornschein and Jorg Lucke Nonlinear spike-and-slab sparse coding for interpretable image encoding
Mengchen Zhu, Ian Stevenson, Urs Köster, Charles M. Gray, Bruno A. Olshausen, and Christopher Rozell: Modeling single-trial V1 population response to dynamic natural scenes