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pro vyhledávání: '"Legenstein, Robert"'
Efficient implementations of spiking neural networks on neuromorphic hardware promise orders of magnitude less power consumption than their non-spiking counterparts. The standard neuron model for spike-based computation on such neuromorphic systems h
Externí odkaz:
http://arxiv.org/abs/2408.07517
Autor:
Ortner, Thomas, Petschenig, Horst, Vasilopoulos, Athanasios, Renner, Roland, Brglez, Špela, Limbacher, Thomas, Piñero, Enrique, Barranco, Alejandro Linares, Pantazi, Angeliki, Legenstein, Robert
There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI models struggle in such scenarios,
Externí odkaz:
http://arxiv.org/abs/2405.05141
Autor:
Özdenizci, Ozan, Legenstein, Robert
Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications. As the progress in neuromorphic computing with SNNs expands their use in applications, the problem of advers
Externí odkaz:
http://arxiv.org/abs/2311.09266
Autor:
Özdenizci, Ozan, Legenstein, Robert
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision trans
Externí odkaz:
http://arxiv.org/abs/2207.14626
Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in b
Externí odkaz:
http://arxiv.org/abs/2205.11276
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
In the paper, we show how scalable, low-cost trunk-like robotic arms can be constructed using only basic 3D-printing equipment and simple electronics. The design is based on uniform, stackable joint modules with three degrees of freedom each. Moreove
Externí odkaz:
http://arxiv.org/abs/2104.04064
Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
Autor:
Korcsak-Gorzo, Agnes, Müller, Michael G., Baumbach, Andreas, Leng, Luziwei, Breitwieser, Oliver Julien, van Albada, Sacha J., Senn, Walter, Meier, Karlheinz, Legenstein, Robert, Petrovici, Mihai A.
Publikováno v:
PLoS Comput Biol 18(3): e1009753 (2022)
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately. Often, this requires visiting multiple interpretations of the available information or mult
Externí odkaz:
http://arxiv.org/abs/2006.11099
Autor:
Kaiser, Jacques, Hoff, Michael, Konle, Andreas, Tieck, J. Camilo Vasquez, Kappel, David, Reichard, Daniel, Subramoney, Anand, Legenstein, Robert, Roennau, Arne, Maass, Wolfgang, Dillmann, Rudiger
Publikováno v:
Frontiers in neurorobotics, volume 13, p81, 2019
The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the bi
Externí odkaz:
http://arxiv.org/abs/2003.01431
Autor:
Yan, Yexin, Kappel, David, Neumaerker, Felix, Partzsch, Johannes, Vogginger, Bernhard, Hoeppner, Sebastian, Furber, Steve, Maass, Wolfgang, Legenstein, Robert, Mayr, Christian
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since b
Externí odkaz:
http://arxiv.org/abs/1903.08500
Autor:
Riminucci, Alberto, Legenstein, Robert
We studied LSMO/Alq3/AlOx/Co molecular spin valves in view of their use as synapses in neuromorphic computing. In neuromorphic computing, the learning ability is embodied in specific changes of the synaptic weight. In this perspective, the relevant p
Externí odkaz:
http://arxiv.org/abs/1903.08624