1,123 research outputs found
Ausbreitung von COVID-19 und Strategien der Eindämmung
No full textDer Beitrag von Viola Priesemann beschäftigt sich mit der matehamtischen Modellierung der Ausbreitungsdynamik der Coronavirus-Pandemie und den Strategien ihrer Eindämmung, insbesondere den Auswirkungen von unterschiedlichen Inzidenzzahlen von Infizierten. Erläutert werden die Mechanismen, das exponentielle Wachstum und die Frage, wie mit mathematischen Modellen die Wirkung und Effektivität von nichtpharmazeutischen Interventionen (Testungen, Kontaktnachverfolgungen, Isolierungen) abgeschätzt werden kann – als Unterstützung der Pandemiebekämpfung. Diskutiert wird ferner der Beitrag des Impffortschritts und sein Einfluss auf verschiedene Öffnungsszenarien.The paper by Viola Priesemann deals with the mathematical modelling of spread dynamics of the coronavirus pandemic and the strategies for its containment esp. the effects of different incidences of infections. It explains the mechanisms, the exponential growth, and how mathematical models can be used to estimate the impact and effectiveness of non-pharmaceutical interventions (testing, contact tracing, isolation) - as support for pandemic control. The contribution of vaccination progress and its influence on different opening scenarios will also be discussed
Can a time varying external drive give rise to apparent criticality in neural systems? - Fig 5
No full text(Color) Avalanche size and duration distributions obtained for a continuously varying IPP are well approximated by power-law distributions with an exponent of -1.5 (A), and are well approximated by the analytical results, shown for bin size 1 (B). The IPP was realized as a sinusoidal with period T=250s and offset 1 (i.e. sin(t/T)+1), as sketched in the inset. The resulting mean rate is unity. Colored lines correspond to different bin sizes, circles depict analytical results, and the dashed black line depicts a reference power law with an exponent of -1.5.</p
25 years of criticality in neuroscience — established results, open controversies, novel concepts
No full textBetween Perfectly Critical and Fully Irregular: A Reverberating Model Captures and Predicts Cortical Spike Propagation
No full textInferring collective dynamical states from widely unobserved systems
No full textWhen assessing spatially extended complex systems, one can rarely sample the states of all components. We show that this spatial subsampling typically leads to severe underestimation of the risk of instability in systems with propagating events. We derive a subsampling-invariant estimator, and demonstrate that it correctly infers the infectiousness of various diseases under subsampling, making it particularly useful in countries with unreliable case reports. In neuroscience, recordings are strongly limited by subsampling. Here, the subsampling-invariant estimator allows to revisit two prominent hypotheses about the brain's collective spiking dynamics: asynchronous-irregular or critical. We identify consistently for rat, cat, and monkey a state that combines features of both and allows input to reverberate in the network for hundreds of milliseconds. Overall, owing to its ready applicability, the novel estimator paves the way to novel insight for the study of spatially extended dynamical systems
Local dendritic balance enables learning of efficient representations in networks of spiking neurons
No full textHow can neural networks learn to efficiently represent complex and
high-dimensional inputs via local plasticity mechanisms? Classical models of
representation learning assume that input weights are learned via pairwise
Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity
only works under unrealistic requirements on neural dynamics and input
statistics. To overcome these limitations, we derive from first principles a
learning scheme based on voltage-dependent synaptic plasticity rules. Here,
inhibition learns to locally balance excitatory input in individual dendritic
compartments, and thereby can modulate excitatory synaptic plasticity to learn
efficient representations. We demonstrate in simulations that this learning
scheme works robustly even for complex, high-dimensional and correlated inputs,
and with inhibitory transmission delays, where Hebbian-like plasticity fails.
Our results draw a direct connection between dendritic excitatory-inhibitory
balance and voltage-dependent synaptic plasticity as observed in vivo, and
suggest that both are crucial for representation learning.Comment: 34 Pages, 14 Figure
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