Modulated escape from a metastable state driven by colored noise
arXiv:1411.0432 · doi:10.1103/PhysRevE.92.052119
Abstract
Many phenomena in nature are described by excitable systems driven by colored noise. The temporal correlations in the fluctuations hinder an analytical treatment. We here present a general method of reduction to a white-noise system, capturing the color of the noise by effective and time-dependent boundary conditions. We apply the formalism to a model of the excitability of neuronal membranes, the leaky integrate-and-fire neuron model, revealing an analytical expression for the linear response of the system valid up to moderate frequencies. The closed form analytical expression enables the characterization of the response properties of such excitable units and the assessment of oscillations emerging in networks thereof.
References in corpus (2)
Cited by in corpus (14)
- The stabilizing effect of volatility in financial markets
- Hybrid scheme for modeling local field potentials from point-neuron networks
- Identifying anatomical origins of coexisting oscillations in the cortical microcircuit
- Spectroscopy and Directed Transport of Topological Solitons in Crystals of Trapped Ions
- Conditions for wave trains in spiking neural networks
- Integration of continuous-time dynamics in a spiking neural network simulator
- Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer's disease
- Exact results for power spectrum and susceptibility of a leaky integrate-and-fire neuron with two-state noise
- Equivalence between synaptic current dynamics and heterogeneous propagation delays in spiking neuron networks
- Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
- Perfect spike detection via time reversal
- Fredholm theory for the mean first-passage time of integrate-and-fire oscillators with colored noise input
- Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
- How the connectivity structure of neuronal networks influences responses to oscillatory stimuli