NICE 2021 - Agenda
(show all abstracts)Tuesday, 16 March 2021 | |||
NICE #8(The NICE#8 was initially planned to take place in 2020, but had to be postponed to 2021 due to COVID-19.) Note: the exact order and timing of the talks is so far draft! Times in the agenda are in CET (Berlin), EDT (New York), PDT (Los Angeles) and UTC. (Some other time zones: Australia, Japan, China, India, ... or only CET ... ) Meeting venue:
NEUROTECH Forum (15 March 2021)Info: just before NICE on 15 March 2021 the NEUROTECH Forum II will take place online (free of charge. Registration for the forum event). The topic is "Neuromorphic Computing Technologies: Opportunities, challenges and Applications Roadmap". | |||
CET: 14:00‑14:10 EDT: 09:00‑09:10 PDT: 06:00‑06:10 UTC: 13:00‑13:10 (10+5 min) | Welcome to NICE #8 | ||
CET: 14:15‑14:40 EDT: 09:15‑09:40 PDT: 06:15‑06:40 UTC: 13:15‑13:40 (25 min) | Organizer Round | ||
CET: 14:40‑15:20 EDT: 09:40‑10:20 PDT: 06:40‑07:20 UTC: 13:40‑14:20 (40+5 min) | Keynote: Lessons from Loihi for the Future of Neuromorphic Computing | Mike Davies (Intel) | |
CET: 15:25‑15:45 EDT: 10:25‑10:45 PDT: 07:25‑07:45 UTC: 14:25‑14:45 (20+5 min) | Why is Neuromorphic Event-based Engineering the future of AI? | Ryad Benjamin Benosman (UPITT/CMU/SORBONNE) | |
CET: 15:50‑16:10 EDT: 10:50‑11:10 PDT: 07:50‑08:10 UTC: 14:50‑15:10 (20+5 min) | The BrainScaleS mobile platform | Johannes Schemmel (Heidelberg University) | |
CET: 16:15‑16:45 EDT: 11:15‑11:45 PDT: 08:15‑08:45 UTC: 15:15‑15:45 (30 min) | (break) | ||
CET: 16:45‑16:55 EDT: 11:45‑11:55 PDT: 08:45‑08:55 UTC: 15:45‑15:55 (10 min) | Group photo (zoom screenshots) | ||
CET: 16:55‑17:15 EDT: 11:55‑12:15 PDT: 08:55‑09:15 UTC: 15:55‑16:15 (20+5 min) | Evaluating complexity and resilience trade-offs in emerging memory inference machines | Christopher Bennett (Sandia National Labs) | |
CET: 17:20‑17:30 EDT: 12:20‑12:30 PDT: 09:20‑09:30 UTC: 16:20‑16:30 (10+5 min) | Lightning talk: From clean room to machine room: towards accelerated cortical simulations on the BrainScaleS wafer-scale system | Sebastian Schmitt (Heidelberg University) | |
CET: 17:35‑17:55 EDT: 12:35‑12:55 PDT: 09:35‑09:55 UTC: 16:35‑16:55 (20+5 min) | Closed-loop experiments on the BrainScaleS-2 architecture | Korbinian Schreiber (Heidelberg University) | |
CET: 18:00‑18:20 EDT: 13:00‑13:20 PDT: 10:00‑10:20 UTC: 17:00‑17:20 (20+5 min) | Batch << 1: Why Neuromorphic Computing Architectures Suit Real-Time Workloads | Jonathan Tapson (GrAI Matter Labs) | |
CET: 18:25‑18:45 EDT: 13:25‑13:45 PDT: 10:25‑10:45 UTC: 17:25‑17:45 (20+5 min) | Neuromorphic and AI research at BCAI (Bosch Center for Artificial Intelligence) | Thomas Pfeil (Bosch Center for Artificial Intelligence) | |
CET: 18:50‑19:10 EDT: 13:50‑14:10 PDT: 10:50‑11:10 UTC: 17:50‑18:10 (20+5 min) | Mapping Deep Neural Networks on SpiNNaker2 | Florian Kelber (TU Dresden) | |
CET: 19:15‑19:45 EDT: 14:15‑14:45 PDT: 11:15‑11:45 UTC: 18:15‑18:45 (30 min) | Open mic / discussion | ||
CET: 19:45 EDT: 14:45 PDT: 11:45 UTC: 18:45 | End of day I | ||
CET: 19:45‑20:45 EDT: 14:45‑15:45 PDT: 11:45‑12:45 UTC: 18:45‑19:45 (60 min) | (break) | ||
CET: 21:00‑00:00 EDT: 16:00‑19:00 PDT: 13:00‑16:00 UTC: 20:00‑23:00 (180 min) | Tutorials: BrainScaleS and DYNAP-SETwo tutorials/hands on in parallel:
For a description please see the tutorials page. |
Wednesday, 17 March 2021 | |||
CET: 10:30‑12:00 EDT: 05:30‑07:00 PDT: 02:30‑04:00 UTC: 09:30‑11:00 (90 min) | Tutorial: SpiNNaker hands-on(Note: the same SpiNNaker hands on tutorial is also offered on Thursday, 21:00 - 22:30h CET) For a description please see the tutorials page. | Andrew Rowley (UMAN) | |
CET: 12:00‑14:00 EDT: 07:00‑09:00 PDT: 04:00‑06:00 UTC: 11:00‑13:00 (120 min) | (break) | ||
CET: 14:00 EDT: 09:00 PDT: 06:00 UTC: 13:00 | NICE - day II | ||
CET: 14:00‑14:40 EDT: 09:00‑09:40 PDT: 06:00‑06:40 UTC: 13:00‑13:40 (40+5 min) | Keynote: From Brains to Silicon -- Applying lessons from neuroscience to machine learning | Jeff Hawkins and Subutai Ahmad (Numenta) | |
CET: 14:45‑15:05 EDT: 09:45‑10:05 PDT: 06:45‑07:05 UTC: 13:45‑14:05 (20+5 min) | A Neuromorphic Future for Classic Computing Tasks | Brad Aimone (Sandia National Laboratories) | |
CET: 15:10‑15:20 EDT: 10:10‑10:20 PDT: 07:10‑07:20 UTC: 14:10‑14:20 (10+5 min) | Lightning talk: Benchmarking of Neuromorphic Hardware Systems | Christoph Ostrau (Bielefeld University) | |
CET: 15:25‑15:45 EDT: 10:25‑10:45 PDT: 07:25‑07:45 UTC: 14:25‑14:45 (20+5 min) | Natural density cortical models as benchmarks for universal neuromorphic computers | Markus Diesmann (Forschungszentrum Jülich GmbH) | |
CET: 15:50‑16:15 EDT: 10:50‑11:15 PDT: 07:50‑08:15 UTC: 14:50‑15:15 (25 min) | Poster Lightning Talks1 min - 1 slide poster appetizers | ||
CET: 16:15‑17:15 EDT: 11:15‑12:15 PDT: 08:15‑09:15 UTC: 15:15‑16:15 (60 min) | Poster session A and coffee | ||
CET: 17:15‑17:35 EDT: 12:15‑12:35 PDT: 09:15‑09:35 UTC: 16:15‑16:35 (20+5 min) | Platform-Agnostic Neural Algorithm Composition using Fugu | William Severa (Sandia National Laboratories) | |
CET: 17:40‑17:50 EDT: 12:40‑12:50 PDT: 09:40‑09:50 UTC: 16:40‑16:50 (10+5 min) | Lightning talk: Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware | Andrew Sornborger (Los Alamos National Laboratory) | |
CET: 17:55‑18:15 EDT: 12:55‑13:15 PDT: 09:55‑10:15 UTC: 16:55‑17:15 (20+5 min) | Inductive bias transfer between brains and machines | Fabian Sinz (University Tübingen) | |
CET: 18:20‑18:30 EDT: 13:20‑13:30 PDT: 10:20‑10:30 UTC: 17:20‑17:30 (10+5 min) | Lightning talk: Spike Latency Reduction generates Efficient Predictive Coding | Pau Vilimelis Aceituno (ETH Zürich) | |
CET: 18:35‑18:45 EDT: 13:35‑13:45 PDT: 10:35‑10:45 UTC: 17:35‑17:45 (10+5 min) | Lightning talk: Cognitive Domain Ontologies: HPCs to Ultra Low Power Neuromorphic Platforms | Chris Yakopcic (University of Dayton) | |
CET: 18:50‑19:20 EDT: 13:50‑14:20 PDT: 10:50‑11:20 UTC: 17:50‑18:20 (30 min) | Open mic / discussion | ||
CET: 19:20‑20:20 EDT: 14:20‑15:20 PDT: 11:20‑12:20 UTC: 18:20‑19:20 (60 min) | (break) |
Thursday, 18 March 2021 | |||
CET: 10:00‑13:00 EDT: 05:00‑08:00 PDT: 02:00‑05:00 UTC: 09:00‑12:00 (180 min) | Tutorial: BrainScaleS hands-on(Note: the same BrainScaleS hands on tutorial is also offered on Tuesday evening)
For a description of the pre-requirements, please see the tutorials page. | ||
CET: 13:00‑14:00 EDT: 08:00‑09:00 PDT: 05:00‑06:00 UTC: 12:00‑13:00 (60 min) | (break) | ||
CET: 14:00 EDT: 09:00 PDT: 06:00 UTC: 13:00 | NICE - day III | ||
CET: 14:00‑14:40 EDT: 09:00‑09:40 PDT: 06:00‑06:40 UTC: 13:00‑13:40 (40+5 min) | Keynote: Biological inspiration for improving computing and learning in spiking neural networks | Wolfgang Maass (Graz University of Technology) | |
CET: 14:45‑15:05 EDT: 09:45‑10:05 PDT: 06:45‑07:05 UTC: 13:45‑14:05 (20+5 min) | On the computational power and complexity of Spiking Neural Networks | Johan Kwisthout (Radboud Universiteit Nijmegen) | |
CET: 15:10‑15:30 EDT: 10:10‑10:30 PDT: 07:10‑07:30 UTC: 14:10‑14:30 (20+5 min) | Evolutionary Optimization for Neuromorphic Systems | Catherine Schuman (Oak Ridge ) | |
CET: 15:35‑15:55 EDT: 10:35‑10:55 PDT: 07:35‑07:55 UTC: 14:35‑14:55 (20+5 min) | An event-based gas sensing device that resolves fast transients in a turbulent environment | Michael Schmuker (University of Hertfordshire) | |
CET: 16:00‑16:20 EDT: 11:00‑11:20 PDT: 08:00‑08:20 UTC: 15:00‑15:20 (20+5 min) | Sequence learning, prediction, and generation in networks of spiking neuronsSequence learning, prediction and generation has been proposed to be the universal computation performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes this form of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context-specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns non-Markovian sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific feedforward subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context-specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay. | Younes Bouhadjar (Forschungszentrum Juelich) | |
CET: 16:25‑17:25 EDT: 11:25‑12:25 PDT: 08:25‑09:25 UTC: 15:25‑16:25 (60 min) | Poster session b and coffee | ||
CET: 17:25‑17:45 EDT: 12:25‑12:45 PDT: 09:25‑09:45 UTC: 16:25‑16:45 (20+5 min) | Walter Senn (Universität Bern) | ||
CET: 17:50‑18:10 EDT: 12:50‑13:10 PDT: 09:50‑10:10 UTC: 16:50‑17:10 (20+5 min) | Conductance-based dendrites perform reliability-weighted opinion pooling | Jakob Jordan (Institute of Physiology, University of Bern) | |
CET: 18:15‑18:25 EDT: 13:15‑13:25 PDT: 10:15‑10:25 UTC: 17:15‑17:25 (10+5 min) | Lightning talk: Natural gradient learning for spiking neurons | Elena Kreutzer (University of Bern) | |
CET: 18:30‑18:50 EDT: 13:30‑13:50 PDT: 10:30‑10:50 UTC: 17:30‑17:50 (20+5 min) | Making spiking neurons more succinct with multi-compartment models | Johannes Leugering (Fraunhofer IIS) | |
CET: 18:55‑19:05 EDT: 13:55‑14:05 PDT: 10:55‑11:05 UTC: 17:55‑18:05 (10+5 min) | Lightning talk: The Computational Capacity of Mem-LRC Reservoirs | Forrest Sheldon (Los Alamos National Lab - T-4/CNLS) | |
CET: 19:10‑19:40 EDT: 14:10‑14:40 PDT: 11:10‑11:40 UTC: 18:10‑18:40 (30 min) | Open mic / discussion | ||
CET: 19:40‑20:50 EDT: 14:40‑15:50 PDT: 11:40‑12:50 UTC: 18:40‑19:50 (70 min) | (break) | ||
CET: 21:00‑22:30 EDT: 16:00‑17:30 PDT: 13:00‑14:30 UTC: 20:00‑21:30 (90 min) | Tutorial: SpiNNaker hands-on(Note: the same SpiNNaker hands on tutorial is also offered on Wednesday, 10:30-12:00h CET) For a description please see the tutorials page. |