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pro vyhledávání: '"Kriener, Laura"'
Spiking Neural Networks (SNNs) have the potential for rich spatio-temporal signal processing thanks to exploiting both spatial and temporal parameters. The temporal dynamics such as time constants of the synapses and neurons and delays have been rece
Externí odkaz:
http://arxiv.org/abs/2407.18838
Autor:
Göltz, Julian, Weber, Jimmy, Kriener, Laura, Lake, Peter, Payvand, Melika, Petrovici, Mihai A.
Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Transmission delays play an important role in shaping these temporal characteristics. Recent work has demonstrated the substantial ad
Externí odkaz:
http://arxiv.org/abs/2404.19165
Autor:
Ellenberger, Benjamin, Haider, Paul, Jordan, Jakob, Max, Kevin, Jaras, Ismael, Kriener, Laura, Benitez, Federico, Petrovici, Mihai A.
How physical networks of neurons, bound by spatio-temporal locality constraints, can perform efficient credit assignment, remains, to a large extent, an open question. In machine learning, the answer is almost universally given by the error backpropa
Externí odkaz:
http://arxiv.org/abs/2403.16933
Autor:
Kriener, Laura, Völk, Kristin, von Hünerbein, Ben, Benitez, Federico, Senn, Walter, Petrovici, Mihai A.
Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks, memories of past activities need to persist on significantly longer timescales than the relaxation times o
Externí odkaz:
http://arxiv.org/abs/2402.16763
Autor:
Göltz, Julian, Billaudelle, Sebastian, Kriener, Laura, Blessing, Luca, Pehle, Christian, Müller, Eric, Schemmel, Johannes, Petrovici, Mihai A.
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico. Here, we discuss several different approaches, including a tentative comparison of the results on BrainScaleS-2, and hint tow
Externí odkaz:
http://arxiv.org/abs/2309.10823
Autor:
Yik, Jason, Berghe, Korneel Van den, Blanken, Douwe den, Bouhadjar, Younes, Fabre, Maxime, Hueber, Paul, Kleyko, Denis, Pacik-Nelson, Noah, Sun, Pao-Sheng Vincent, Tang, Guangzhi, Wang, Shenqi, Zhou, Biyan, Ahmed, Soikat Hasan, Joseph, George Vathakkattil, Leto, Benedetto, Micheli, Aurora, Mishra, Anurag Kumar, Lenz, Gregor, Sun, Tao, Ahmed, Zergham, Akl, Mahmoud, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Bogdan, Petrut, Bohte, Sander, Buckley, Sonia, Cauwenberghs, Gert, Chicca, Elisabetta, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Fischer, Tobias, Forest, Jeremy, Fra, Vittorio, Furber, Steve, Furlong, P. Michael, Gilpin, William, Gilra, Aditya, Gonzalez, Hector A., Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Liu, Yao-Hong, Liu, Shih-Chii, Ma, Haoyuan, Manohar, Rajit, Margarit-Taulé, Josep Maria, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Panda, Priyadarshini, Park, Jongkil, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Pierro, Alessandro, Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens JS, van Schaik, André, Schemmel, Johannes, Schmidgall, Samuel, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Shrestha, Sumit Bam, Sifalakis, Manolis, Sironi, Amos, Stewart, Matthew, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Timcheck, Jonathan, Tömen, Nergis, Urgese, Gianvito, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Zohora, Fatima Tuz, Frenkel, Charlotte, Reddi, Vijay Janapa
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accu
Externí odkaz:
http://arxiv.org/abs/2304.04640
Autor:
Max, Kevin, Kriener, Laura, García, Garibaldi Pineda, Nowotny, Thomas, Jaras, Ismael, Senn, Walter, Petrovici, Mihai A.
Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas. In deep learning, a known solution is error backpropagation, which however requires biologically implausible weight transport from fee
Externí odkaz:
http://arxiv.org/abs/2212.10249
Autor:
Haider, Paul, Ellenberger, Benjamin, Kriener, Laura, Jordan, Jakob, Senn, Walter, Petrovici, Mihai A.
The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag. This inherent property of physical dynamical systems results in delay
Externí odkaz:
http://arxiv.org/abs/2110.14549
The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in early-stage prototyping scenari
Externí odkaz:
http://arxiv.org/abs/2102.08211
Autor:
Göltz, Julian, Kriener, Laura, Baumbach, Andreas, Billaudelle, Sebastian, Breitwieser, Oliver, Cramer, Benjamin, Dold, Dominik, Kungl, Akos Ferenc, Senn, Walter, Schemmel, Johannes, Meier, Karlheinz, Petrovici, Mihai Alexandru
Publikováno v:
Nature Machine Intelligence 3, 823-835 (2021)
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for short time-to-solution and low energy-to-solution characteristics. At the
Externí odkaz:
http://arxiv.org/abs/1912.11443