Stability of stochastic semigroups and applications to Stein's neuronal model
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Publication:1678318
DOI10.3934/dcdsb.2018026OpenAlexW2766494764MaRDI QIDQ1678318
Katarzyna Pichór, Ryszard Rundnicki
Publication date: 14 November 2017
Published in: Discrete and Continuous Dynamical Systems. Series B (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3934/dcdsb.2018026
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Cites Work
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- Stein's neuronal model with pooled renewal input
- Asymptotic decomposition of substochastic operators and semigroups
- Chaos, fractals, and noise: Stochastic aspects of dynamics.
- Continuous Markov semigroups and stability of transport equations
- An analysis of Stein's model for stochastic neuronal excitation
- A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input
- Stochastic Operators and Semigroups and Their Applications in Physics and Biology
- Convergence of One-Parameter Operator Semigroups
- Piecewise Deterministic Markov Processes in Biological Models
- Introduction to Theoretical Neurobiology
- Markov Chains
- Asymptotic decomposition of substochastic semigroups and applications
- An introduction to continuous-time stochastic processes. Theory, models, and applications to finance, biology, and medicine.
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