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Using ODE waveform-relaxation methods to efficiently include gap junctions in distributed neural network simulations

Waveform-relaxation methods divide systems of differential equations into subsystems and therefore allow for parallelization across the system. Here we present an application for ODE waveform-relaxation methods in the context of spiking neural networ

Externí odkaz: http://arxiv.org/abs/2105.06404

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Event-based update of synapses in voltage-based learning rules

Autor: Stapmanns, Jonas, Hahne, Jan, Helias, Moritz, Bolten, Matthias, Diesmann, Markus, Dahmen, David

Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third f

Externí odkaz: http://arxiv.org/abs/2009.08667

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Integration of continuous-time dynamics in a spiking neural network simulator

Autor: Hahne, Jan, Dahmen, David, Schuecker, Jannis, Frommer, Andreas, Bolten, Matthias, Helias, Moritz, Diesmann, Markus

Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units. The unified simulation framework presented here supports the co

Externí odkaz: http://arxiv.org/abs/1610.09990

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Akademický článek

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Advanced plasticity rules in NEST Clopath and Urbanczik-Senn plasticity

Autor: Stapmanns, Jonas, Dahmen, David, Hahne, Jan, Helias, Moritz, Bolten, Matthias, Diesmann, Markus

Publikováno v: 7-th HBP Summit, Athens, Greece, 2020-02-03-2020-02-06

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3364::9990f37521c4d65c32549e022de98adc
https://juser.fz-juelich.de/record/874229

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Waveform-relaxation methods for ordinary and stochastic dierential equations with applications in distributed neural network simulations

Autor: Hahne, Jan

This thesis deals with the efficient solution of large systems of ordinary and stochastic differential equations with waveform-relaxation techniques in the context of spiking neural network simulators. Parallel spiking neuronal network simulators mak

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______711::1b0d14f333ac3e65aaaa9b89ce6f0813
http://elpub.bib.uni-wuppertal.de/edocs/dokumente/fbc/mathematik/diss2018/hahne

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[Re] A neural model of the saccade generator in the reticular formation

Autor: Senden, Mario, Schuecker, Jannis, Hahne, Jan, Diesmann, Markus, Goebel, Rainer

Publikováno v: ReScience C, 4(1):3. The ReScience Journal
ReScience

Reference implementation of Gancarz, G., Grossberg, S. "A Neural Model of the Saccade Generator in the Reticular Formation." Neural Networks 11, no. 7-8 (October 1998): 1159-74. doi:10.1016/S0893-6080(98)00096-3 The model has been implemented in the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13bce78f1c86987a416cbbb3a5cb2dbb
https://cris.maastrichtuniversity.nl/en/publications/a88adbe1-4877-4da1-9bda-55d148056cdb

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