Search Results for author: Markus Diesmann

Found 11 papers, 4 papers with code

Sequence learning in a spiking neuronal network with memristive synapses

no code implementations29 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.


Sequence learning, prediction, and replay in networks of spiking neurons

no code implementations5 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.

Connectivity Concepts in Neuronal Network Modeling

no code implementations6 Oct 2021 Johanna Senk, Birgit Kriener, Mikael Djurfeldt, Nicole Voges, Han-Jia Jiang, Lisa Schüttler, Gabriele Gramelsberger, Markus Diesmann, Hans E. Plesser, Sacha J. van Albada

We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.

Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring

no code implementations23 Sep 2021 Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, Susanne Kunkel

With growing network size a compute node receives spikes from an increasing number of different source neurons until in the limit each synapse on the compute node has a unique source.

Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution

no code implementations11 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.

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

1 code implementation25 May 2018 Johanna Senk, Espen Hagen, Sacha J. van Albada, Markus Diesmann

Based on model predictions of spiking activity and LFPs, we find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental data recorded in the sensory cortex.

LFP beta amplitude is predictive of mesoscopic spatio-temporal phase patterns

1 code implementation28 Mar 2017 Michael Denker, Lyuba Zehl, Bjørg E. Kilavik, Markus Diesmann, Thomas Brochier, Alexa Riehle, Sonja Grün

Beta oscillations observed in motor cortical local field potentials (LFPs) recorded on separate electrodes of a multi-electrode array have been shown to exhibit non-zero phase shifts that organize into a planar wave propagation.

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