no code implementations • 10 Dec 2022 • Agnes Korcsak-Gorzo, Charl Linssen, Jasper Albers, Stefan Dasbach, Renato Duarte, Susanne Kunkel, Abigail Morrison, Johanna Senk, Jonas Stapmanns, Tom Tetzlaff, Markus Diesmann, Sacha J. van Albada
This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience.
no code implementations • 29 Nov 2022 • Younes Bouhadjar, Sebastian Siegel, Tom Tetzlaff, Markus Diesmann, Rainer Waser, Dirk J. Wouters
In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model.
no code implementations • 21 Jun 2022 • Younes Bouhadjar, Dirk J. Wouters, Markus Diesmann, Tom Tetzlaff
Animals rely on different decision strategies when faced with ambiguous or uncertain cues.
1 code implementation • 16 Dec 2021 • 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
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease.
no code implementations • 5 Nov 2021 • Younes Bouhadjar, Dirk J. Wouters, Markus Diesmann, Tom Tetzlaff
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.
no code implementations • 11 May 2021 • Stefan Dasbach, Tom Tetzlaff, Markus Diesmann, Johanna Senk
For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution.