Organizers: | |
|---|---|
Abstract: | In this session, we will discuss theoretical frameworks for building principled neuromorphic algorithms and the features that make an ideal framework for Loihi. We will discuss efforts to develop a robust and flexible system that can go beyond networks that perform just a single task. As well, we will cover how proposed frameworks might synthesize into a high-level abstraction paradigm for building general purpose algorithms. |
Speakers | Friedrich Sommer: Taxonomy of conceptual frameworks and neuromorphic VSA Paxon Frady: Frameworks for efficient coding and computation on neuromorphic hardware Gregor Schöner: Dynamic Field Theory as a theoretical framework for neuromorphic embodied cognition Christian Tetzlaff: The usage of on-chip learning for universal computing Hyeryung Jang: Information-Theoretic Principles for Neuromorphic Computing: Information Bottleneck and Bayesian Learning Gregor Lenz: Efficient ANN-SNN conversion |
Reference material | https://www.pnas.org/content/pnas/116/36/18050.full.pdf https://redwood.berkeley.edu/wp-content/uploads/2020/08/resonator1.pdf |
Recording | Not yet available |
Link to Presentation | Not yet available |
Please use the comment section on this page to ask questions or comment about this specific presentation.