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The goal of this workshop is to bring together researchers from neuroscience, deep learning, machine learning, computer science theory, and statistics for a rich discussion about how computer science and neuroscience can inform one another as these two fields rapidly move forward. We invite high quality submissions and discussion on topics including, but not limited to, the following fundamental questions: a) shared approaches for analyzing biological and artificial neural systems, b) how insights and challenges from neuroscience can inspire progress in machine learning, and c) methods for interpreting the revolutionary large scale datasets produced by new experimental neuroscience techniques.
Experimental methods for measuring neural activity and structure have undergone recent revolutionary advances, including in high-density recording arrays, population calcium imaging, and large-scale reconstructions of anatomical circuitry. These developments promise unprecedented insights into the collective dynamics of neural populations and thereby the underpinnings of brain-like computation. However, these next-generation methods for measuring the brain’s architecture and function produce high-dimensional, large scale, and complex datasets, raising challenges for analysis. What are the machine learning and analysis approaches that will be indispensable for analyzing these next-generation datasets? What are the computational bottlenecks and challenges that must be overcome?
In parallel to experimental progress in neuroscience, the rise of deep learning methods has shown that hard computational problems can be solved by machine learning algorithms that are inspired by biological neural networks, and built by cascading many nonlinear units. In contrast to the brain, artificial neural systems are fully observable, so that experimental data-collection constraints are not relevant. Nevertheless, it has proven challenging to develop a theoretical understanding of how neural networks solve tasks, and what features are critical to their performance. Thus, while deep networks differ from biological neural networks in many ways, they provide an interesting testing ground for evaluating strategies for understanding neural processing systems. Are there synergies between analysis methods for biological and artificial neural systems? Has the resurgence of deep learning resulted in new hypotheses or strategies for trying to understand biological neural networks? Conversely, can neuroscience provide inspiration for the next generation of machine-learning algorithms?
We welcome participants from a range of disciplines in statistics, applied physics, machine learning, and both theoretical and experimental neuroscience, with the goal of fostering interdisciplinary insights. We hope that active discussions among these groups can set in motion new collaborations and facilitate future breakthroughs on fundamental research problems.
Thu 11:30 p.m. - 11:45 p.m.
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Welcome and Opening Remarks
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Talk
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Alyson Fletcher · Konrad P Koerding 🔗 |
Thu 11:45 p.m. - 12:30 a.m.
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Christos Papadimitriou : A computer scientist thinks about the brain ( Keynote ) link » | Christos Papadimitriou 🔗 |
Fri 12:30 a.m. - 1:00 a.m.
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Cristina Savin : Spike-Based Probabilistic Computation
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Talk
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Cristina Savin 🔗 |
Fri 1:00 a.m. - 1:30 a.m.
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Mitya Chklovskii : Toward Biologically Plausible Machine Learning: A Similarity Matching Approach
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Talk
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Dmitri B Chklovskii 🔗 |
Fri 1:30 a.m. - 2:00 a.m.
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Coffee Break 1a (plus posters)
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🔗 |
Fri 2:00 a.m. - 2:30 a.m.
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Jonathan Pillow : Scalable Inference for Structured Hierarchical Receptive Field Models ( Talk ) link » | Jonathan W Pillow 🔗 |
Fri 2:30 a.m. - 3:00 a.m.
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Emily Fox : Functional Connectivity in MEG via Graphical Models of Time Series ( Talk ) link » | Emily Fox 🔗 |
Fri 3:00 a.m. - 3:30 a.m.
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Srini Turaga : Independence testing & Amortized inference, with neural networks, for neuroscience
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Talk
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Srinivas C Turaga 🔗 |
Fri 3:30 a.m. - 5:00 a.m.
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Lunch Day 1
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Lunch
)
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🔗 |
Fri 5:00 a.m. - 5:30 a.m.
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Il Memming Park : Dynamical Systems Interpretation of Neural Trajectories ( Talk ) link » | Il Memming Park 🔗 |
Fri 5:30 a.m. - 6:00 a.m.
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David Sussillo : LFADS - Latent Factor Analysis via Dynamical Systems
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Talk
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David Sussillo 🔗 |
Fri 6:00 a.m. - 6:30 a.m.
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Coffee Break 1b (plus posters)
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Fri 6:30 a.m. - 7:00 a.m.
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Poster Session 1
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Poster Session
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Fri 7:00 a.m. - 7:30 a.m.
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Eva Dyer
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Talk
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Eva Dyer 🔗 |
Fri 7:30 a.m. - 8:00 a.m.
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Michael Buice
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Talk
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🔗 |
Fri 8:00 a.m. - 8:30 a.m.
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Stefan Mihalas : Modeling Optimal Context Integration in Cortical Columns
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Talk
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Stefan Mihalas 🔗 |
Fri 8:30 a.m. - 9:00 a.m.
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Breakout Discussion Afternoon Session
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Breakout Discussion
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Konrad P Koerding 🔗 |
Fri 11:30 p.m. - 11:45 p.m.
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Opening Remarks
(
Talk
)
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Jascha Sohl-Dickstein 🔗 |
Fri 11:45 p.m. - 12:30 a.m.
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Yoshua Bengio : Toward Biologically Plausible Deep Learning
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Keynote
)
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Yoshua Bengio 🔗 |
Sat 12:30 a.m. - 1:00 a.m.
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Surya Ganguli : Deep Neural Models of the Retinal Response to Natural Stimuli
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Talk
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Surya Ganguli 🔗 |
Sat 1:00 a.m. - 1:30 a.m.
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Max Welling : Making Deep Learning Efficient Through Sparsification
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Talk
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Max Welling 🔗 |
Sat 1:30 a.m. - 2:00 a.m.
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Coffee Break 2a (plus posters)
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🔗 |
Sat 2:00 a.m. - 2:30 a.m.
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David Cox : Predictive Coding for Unsupervised Feature Learning
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Talk
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David Cox 🔗 |
Sat 2:30 a.m. - 3:30 a.m.
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From Brains to Bits and Back Again
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Discussion Panel
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Yoshua Bengio · Terrence Sejnowski · Christos H Papadimitriou · Jakob H Macke · Demis Hassabis · Alyson Fletcher · Andreas Tolias · Jascha Sohl-Dickstein · Konrad P Koerding 🔗 |
Sat 3:30 a.m. - 5:00 a.m.
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Lunch Day 2
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🔗 |
Sat 5:00 a.m. - 5:30 a.m.
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Fred Hamprecht : Motif Discovery in Functional Brain Data
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Talk
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Fred Hamprecht 🔗 |
Sat 5:30 a.m. - 6:00 a.m.
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Anima Anandkumar
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Talk
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Anima Anandkumar 🔗 |
Sat 6:00 a.m. - 6:30 a.m.
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Coffee Break 2b (plus posters)
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🔗 |
Sat 6:30 a.m. - 7:00 a.m.
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Poster Session 2
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Poster Session
)
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🔗 |
Sat 7:00 a.m. - 7:30 a.m.
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Kanitscheider : Training Recurrent Networks to Generate Hypotheses About How the Brain Solves Hard Navigation Problems
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Talk
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Ingmar Kanitscheider 🔗 |
Sat 7:30 a.m. - 8:00 a.m.
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Jorg Lucke : Probabilistic Inference and the Brain: Towards General, Scalable, and Deep Approximations
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Talk
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Jörg Lücke 🔗 |
Author Information
Alyson Fletcher (UCLA)
Eva Dyer (Georgia Institute of Technology)
Eva Dyer is an Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University. Dr. Dyer completed her M.S. and Ph.D degrees in Electrical & Computer Engineering at Rice University in 2011 and 2014, respectively. Eva's research interests lie at the intersection of data science, machine learning, and neuroscience.
Jascha Sohl-Dickstein (Google Brain)
Joshua T Vogelstein (The Johns Hopkins University)
Konrad Koerding
Jakob H Macke (research center caesar, an associate of the Max Planck Society)
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2016 : Opening Remarks »
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2016 : Eva Dyer »
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2016 Poster: Exponential expressivity in deep neural networks through transient chaos »
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2015 : Computational discussion: High-density electrical recordings »
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2015 Poster: Unlocking neural population non-stationarities using hierarchical dynamics models »
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2015 Poster: Deep Knowledge Tracing »
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2014 Workshop: Large scale optical physiology: From data-acquisition to models of neural coding »
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2014 Poster: A Bayesian model for identifying hierarchically organised states in neural population activity »
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2014 Poster: Scalable Inference for Neuronal Connectivity from Calcium Imaging »
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2014 Spotlight: A Bayesian model for identifying hierarchically organised states in neural population activity »
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2014 Spotlight: Scalable Inference for Neuronal Connectivity from Calcium Imaging »
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2014 Poster: Low-dimensional models of neural population activity in sensory cortical circuits »
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2013 Workshop: Acquiring and Analyzing the Activity of Large Neural Ensembles »
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2013 Workshop: High-dimensional Statistical Inference in the Brain »
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2013 Poster: Inferring neural population dynamics from multiple partial recordings of the same neural circuit »
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2013 Spotlight: Inferring neural population dynamics from multiple partial recordings of the same neural circuit »
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2012 Poster: Training sparse natural image models with a fast Gibbs sampler of an extended state space »
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2012 Poster: Spectral learning of linear dynamics from generalised-linear observations with application to neural population data »
Lars Buesing · Jakob H Macke · Maneesh Sahani -
2012 Oral: Spectral learning of linear dynamics from generalised-linear observations with application to neural population data »
Lars Buesing · Jakob H Macke · Maneesh Sahani -
2012 Poster: Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning »
Ulugbek S Kamilov · Sundeep Rangan · Alyson Fletcher · MIchael Unser -
2011 Oral: Empirical models of spiking in neural populations »
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2011 Poster: Empirical models of spiking in neural populations »
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2011 Poster: Neural Reconstruction with Approximate Message Passing (NeuRAMP) »
Alyson Fletcher · Sundeep Rangan · Lav R Varshney · Aniruddha Bhargava -
2011 Poster: How biased are maximum entropy models? »
Jakob H Macke · Iain Murray · Peter E Latham -
2011 Poster: Inferring spike-timing-dependent plasticity from spike train data »
Ian H Stevenson · Konrad Koerding -
2010 Poster: Mixture of time-warped trajectory models for movement decoding »
Elaine A Corbett · Eric J Perreault · Konrad Koerding -
2009 Poster: Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis »
Alyson Fletcher · Sundeep Rangan -
2009 Poster: Structural inference affects depth perception in the context of potential occlusion »
Ian H Stevenson · Konrad Koerding -
2009 Spotlight: Structural inference affects depth perception in the context of potential occlusion »
Ian H Stevenson · Konrad Koerding -
2009 Spotlight: Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis »
Alyson Fletcher · Sundeep Rangan -
2009 Poster: Bayesian estimation of orientation preference maps »
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2009 Poster: Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing »
Sundeep Rangan · Alyson Fletcher · Vivek K Goyal -
2009 Spotlight: Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing »
Sundeep Rangan · Alyson Fletcher · Vivek K Goyal -
2008 Poster: Resolution Limits of Sparse Coding in High Dimensions »
Alyson Fletcher · Sundeep Rangan · Vivek K Goyal -
2007 Oral: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior »
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2007 Poster: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior »
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2007 Poster: Receptive Fields without Spike-Triggering »
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2006 Poster: Inducing Metric Violations in Human Similarity Judgements »
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