Zobrazeno 1 - 10
of 242
pro vyhledávání: '"Markus Diesmann"'
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 5, p e1010989 (2023)
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type o
Externí odkaz:
https://doaj.org/article/277153b528b544fabfd2ffed2643ff5f
Autor:
James B. Aimone, Omar Awile, Markus Diesmann, James C. Knight, Thomas Nowotny, Felix Schürmann
Publikováno v:
Frontiers in Neuroinformatics, Vol 17 (2023)
Externí odkaz:
https://doaj.org/article/b1286af0a637415b9b9d0e120a8a1bb1
Autor:
Johanna Senk, Birgit Kriener, Mikael Djurfeldt, Nicole Voges, Han-Jia Jiang, Lisa Schüttler, Gabriele Gramelsberger, Markus Diesmann, Hans E Plesser, Sacha J van Albada
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 9, p e1010086 (2022)
Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, o
Externí odkaz:
https://doaj.org/article/afafb13c753d4821b16e6e51f46ad622
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 6, p e1010233 (2022)
Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervi
Externí odkaz:
https://doaj.org/article/5fc2b991d4a34413bfcc6ac9380af0f1
Autor:
Jasper Albers, Jari Pronold, Anno Christopher Kurth, Stine Brekke Vennemo, Kaveh Haghighi Mood, Alexander Patronis, Dennis Terhorst, Jakob Jordan, Susanne Kunkel, Tom Tetzlaff, Markus Diesmann, Johanna Senk
Publikováno v:
Frontiers in Neuroinformatics, Vol 16 (2022)
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availabil
Externí odkaz:
https://doaj.org/article/0655fab413104b4595212fcfc1109d72
Autor:
Younes Bouhadjar, Sebastian Siegel, Tom Tetzlaff, Markus Diesmann, Rainer Waser, Dirk J Wouters
Publikováno v:
Neuromorphic Computing and Engineering, Vol 3, Iss 3, p 034014 (2023)
Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies
Externí odkaz:
https://doaj.org/article/b584c0f840e34cf08b020ef6a6551b78
Autor:
Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, Susanne Kunkel
Publikováno v:
Frontiers in Neuroinformatics, Vol 15 (2022)
Generic simulation code for spiking neuronal networks spends the major part of the time in the phase where spikes have arrived at a compute node and need to be delivered to their target neurons. These spikes were emitted over the last interval betwee
Externí odkaz:
https://doaj.org/article/472df1a0960b4ccbad77c0b42bf72f4c
Autor:
Arne Heittmann, Georgia Psychou, Guido Trensch, Charles E. Cox, Winfried W. Wilcke, Markus Diesmann, Tobias G. Noll
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2022)
This article employs the new IBM INC-3000 prototype FPGA-based neural supercomputer to implement a widely used model of the cortical microcircuit. With approximately 80,000 neurons and 300 Million synapses this model has become a benchmark network fo
Externí odkaz:
https://doaj.org/article/f8d92255d7ae48ea8563367092701ad8
Autor:
David Dahmen, Moritz Layer, Lukas Deutz, Paulina Anna Dąbrowska, Nicole Voges, Michael von Papen, Thomas Brochier, Alexa Riehle, Markus Diesmann, Sonja Grün, Moritz Helias
Publikováno v:
eLife, Vol 11 (2022)
Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet, this parallel activity is often confined to relatively low-dimensional manifolds. This im
Externí odkaz:
https://doaj.org/article/43004154ed83436da2c38a914386305b
Autor:
Ryota Kobayashi, Shuhei Kurita, Anno Kurth, Katsunori Kitano, Kenji Mizuseki, Markus Diesmann, Barry J. Richmond, Shigeru Shinomoto
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Current techniques have enabled the simultaneous collection of spike train data from large numbers of neurons. Here, the authors report a method to infer the underlying neural circuit connectivity diagram based on a generalized linear model applied t
Externí odkaz:
https://doaj.org/article/1139b3b3aec44e7da4d4083a6c74e53b