22 citations · 53 across the 8 of their papers we have counts for
9 papers
Event-driven eligibility propagation in large sparse networks: efficiency shaped by biological realism
Agnes Korcsak-Gorzo, Jesús A. Espinoza Valverde, Jonas Stapmanns +5
Despite remarkable technological advances, AI systems may still benefit from biological principles, such as recurrent connectivity and energy-efficient mechanisms. Drawing inspirat…
On the stability of event-based control with neuronal dynamics
Luke Eilers, Jonas Stapmanns, Catarina Dias +1
Event-based control, unlike analogue control, poses significant analytical challenges due to its hybrid dynamics. This work investigates the stability and inter-event time properti…
Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Jonas Stapmanns, Catarina Dias, Luke Eilers +2
Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and cost constraints. We compute a…
Capacity-Achieving Input Distribution of the Additive Uniform Noise Channel With Peak Amplitude and Cost Constraint
Jonas Stapmanns, Catarina Dias, Luke Eilers +1
Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and power constraints. We compute…
Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
Bruno Golosio, Jose Villamar, Gianmarco Tiddia +6
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also ho…
Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density
Agnes Korcsak-Gorzo, Charl Linssen, Jasper Albers +9
This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for em…
Gell-Mann-Low criticality in neural networks
Lorenzo Tiberi, Jonas Stapmanns, Tobias Kühn +3
Criticality is deeply related to optimal computational capacity. The lack of a renormalized theory of critical brain dynamics, however, so far limits insights into this form of bio…
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…
Self-consistent formulations for stochastic nonlinear neuronal dynamics
Jonas Stapmanns, Tobias Kühn, David Dahmen +3
Neural dynamics is often investigated with tools from bifurcation theory. However, many neuron models are stochastic, mimicking fluctuations in the input from unknown parts of the…