210 citations · 1.5k across the 62 of their papers we have counts for
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Unfolding recurrence by Green's functions for optimized reservoir computing
Sandra Nestler, Christian Keup, David Dahmen +3
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics. This sets them apart from deep feed-forward networks. Despite the tremendous progress in the…
Large Deviations Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
Alexander van Meegen, Tobias Kühn, Moritz Helias
We here unify the field theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we s…
Event-based update of synapses in voltage-based learning rules
Jonas Stapmanns, Jan Hahne, Moritz Helias +3
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 plasticit…
Momentum-dependence in the infinitesimal Wilsonian renormalization group
Moritz Helias
Wilson's original formulation of the renormalization group is perturbative in nature. We here present an alternative derivation of the infinitesimal momentum shell RG, akin to the…
Transient chaotic dimensionality expansion by recurrent networks
Christian Keup, Tobias Kühn, David Dahmen +1
Neurons in the brain communicate with spikes, which are discrete events in time and value. Functional network models often employ rate units that are continuously coupled by analog…