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A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

, , , , , , and . Frontiers Neuroinformatics, (2015)

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Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator., , , , , , and . Frontiers Neuroinformatics, (2017)Neuronal architecture extracts statistical temporal patterns., , and . CoRR, (2023)The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks., , , , and . PLoS Comput. Biol., (2020)Speed Limits for Deep Learning., , , and . CoRR, (2023)NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models., , , , , and . Frontiers Neuroinformatics, (2022)Including Gap Junctions into Distributed Neuronal Network Simulations., , , , , , , , and . BrainComp, volume 10087 of Lecture Notes in Computer Science, page 43-57. Springer, (2015)Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions., , , and . Frontiers Neuroinformatics, (2020)Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex., , , and . PLoS Comput. Biol., (2012)Equilibrium and response properties of the integrate-and-fire neuron in discrete time., , , and . Frontiers Comput. Neurosci., (2009)Statistical field theory for neural networks, and . (2019)cite arxiv:1901.10416.