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NICE Workshop


February 23
8:00-8:10 Welcome/Logistics
8:10-8:30 Bruce Hendrickson m1
8:30-9:30 Programmatic Panel: Dan Hammerstrom, Jacob Vogelstein, Robinson Pino, Karlheinz Meier, Ken Whang m2 m3 m4
9:30-10:00 Brad Aimone m5
10:00-10:15 break
10:15-11:00 Sebastian Seung (plenary 1) m6
11:00-11:30 Tai Sing Lee m7
11:30-12:00 Xaq Pitkow m8
12:00-1:15 lunch
1:15-2:00 Gary Marcus
(plenary 2)
2:00-2:30 Lars Buesing m10
2:30-3:00 Jeff Hawkins m11
3:00-3:15 break
3:15-3:45 Alan Yuile m12
3:45-4:15 Randal O'Reilly m13
4:15-4:45 Konrad Kording m14
4:45-6:00 Round-table/
February 24
8:00-8:10 Welcome/previous
day overview
8:10-8:30 Ken Whang t1
8:30-8:50 Kris Carlson t2
8:50-9:10 Dhireesha Kudithipudi t3
9:10-9:30 Jeniffer Hasler t4
9:30-10:00 Winfried Wilcke t5
10:00-10:15 break
10:15-10:45 Kevin Gomez t6
10:45-11:15 Steve Furber t7
11:15-11:45 Ralph Etienne-Cummings t8
11:45-12:15 Murat Okandan t9
12:15-1:30 lunch
1:30-2:00 Nathan Gouwens t10
2:00-2:30 Paul Franzon t11
2:30-3:00 Jeremy Freeman t12
3:00-3:15 break
3:15-3:45 Sek Chai t13
3:45-4:15 Paul Rhodes t14
4:15-4:45 Karlheinz Meier (technical) t15
4:45-6:00 Round-table/
February 25
8:00-8:10 Welcome/previous
day overview
8:10-8:30 Tarek Taha w1
8:30-8:50 Fred Rothganger w2
8:50-9:10 Marwan Jabri w3
9:10-9:30 Catherine Schuman, Chris Daffron w4
9:30-10:00 Garrett Kenyon w5
10:00-10:15 break
10:15-10:45 Igor Ovchinnikov w6
10:45-11:05 Helen Li w7
11:05-11:25 Matt Marinella w8
11:25-11:45 Roman Ormandy w9
11:45-12:00 Wrap-up/
Next steps/
Workshop adjourns
12:00-1:30 lunch
1:30-4:00 ''Neuromorphic
Computing Strategy
Paper" open session


Brad Aimone, Sandia, Adaptive Neural Algorithms: the What, Why, and How
Lars Buesing, Columbia, Characterizing Activity Dynamics of Neural Populations: Theoretical and Statistical Perspectives
Kris Carlson, UC Irvine, Large-Scale, Biologically Detailed Neuromorphic Networks: Taming the Beast
Sek Chai, SRI International, Computational Noise Resiliency in Deep Learning Architectures
Chris Daffron, University of Tennessee, Knoxville
Ralph Etienne-Cummings, NYU, Seeing with Spikes: From Motion Detection to Object Recognition
Paul Franzon, North Carolina State University, Hardware Acceleration of Sparse Cognitive Algorithms
Jeremy Freeman, Janelia Farm/HHMI, Measuring and manipulating neural computation
Steve Furber, University of Manchester, The SpiNNaker Project
Kevin Gomez, Seagate, Neuromorphic computing in the Cloud - Implications of Hyperscale Datacenter Trends
Nathan Gouwens, Allen Institute for Brain Science, High-throughput experimental and computational characterization of cortical circuit components
Dan Hammerstrom, DARPA, Neurocomputing - One Perspective
Jennifer Hasler, Georgia Tech: Moving Towards Large, Power-Efficient Neuromorphic Systems
Jeff Hawkins, Numenta, Reverse Engineering the Neocortex: Implications for Machine Learning and Machine Intelligence
Bruce Hendrickson, Sandia, Computing Research Division Director, A Nice Time to be NICE
Marwan Jabri, Neuromorphic LLC, Biologically-Inspired Unsupervised Learning of Higher Order Visual Features
Garrett Kenyon, LANL, Deep, Sparse Representations of Form Depth and Motion
Konrad Kording, Northwestern University, At which level do we want to be neuro-inspired?
Dhireesha Kudithipudi, Rochester Institute of Technology, Traversing the Application Landscape of Neuromemristive Computing
Tai Sing Lee, Carnegie Mellon, Neural circuits for learning internal models of the environment
Helen Li, University of Pittsburgh, The Neuromorphic Computing Leveraging Emerging Devices
Gary Marcus, NYU, The Atoms of Neural Computation
Matt Marinella, Sandia, Resistive Memory for Neuromorphic Algorithm Acceleration
Karlheinz Meier, University of Heidelberg, Mixed-signal accelerated Systems - Recent Progress and Results
Randal O’Reilly, University of Colorado Boulder, Biologically-inspired error driven learning in thalamocortical circuits
Roman Ormandy, Embody Corporation, Multi-modal, Wearable Personal Assistants
Igor Ovchinnikov, UCLA, Toward Cohomological Neurodynamics
Robinson Pino, DOE Office of Science, Neuromorphic Computing for Scientific Discovery
Xaq Pitkow, Rice University, How can we know if the brain is doing a good job?
Paul Rhodes, Evolved Machines, Quantifying the Utility of Artificial Neural Circuitry
Fred Rothganger, Sandia, Can Memristors Learn?
Catherine Schuman, University of Tennessee, Knoxville, A Programmable Array of Neuromorphic Elements
Sebastian Seung, Princeton, Connectomics: from retina to cortex
Tarek Taha, University of Dayton, Neuromorphic Computing on Memristor Cross-Bar Arrays
Jacob Vogelstein, IARPA, Approaches to Advancing Neural Computing
Lloyd Watts, Sandia, Event-Driven Simulation of Spiking Neural Networks
Ken Whang, NSF, Neuro-Inspired Computing at NSF
Winfried Wilcke, IBM, The IBM Cortical Learning Center Project
Alan Yuille, UCLA, Complexity and Compositionality

Please address comments or questions to Linda Wood | Last Modified: