305 research outputs found
Crossover between Levy and Gaussian regimes in first passage processes
We propose a new approach to the problem of the first passage time. Our
method is applicable not only to the Wiener process but also to the
non--Gaussian Lvy flights or to more complicated stochastic
processes whose distributions are stable. To show the usefulness of the method,
we particularly focus on the first passage time problems in the truncated
Lvy flights (the so-called KoBoL processes), in which the
arbitrarily large tail of the Lvy distribution is cut off. We
find that the asymptotic scaling law of the first passage time distribution
changes from -law (non-Gaussian Lvy
regime) to -law (Gaussian regime) at the crossover point. This result
means that an ultra-slow convergence from the non-Gaussian Lvy
regime to the Gaussian regime is observed not only in the distribution of the
real time step for the truncated Lvy flight but also in the
first passage time distribution of the flight. The nature of the crossover in
the scaling laws and the scaling relation on the crossover point with respect
to the effective cut-off length of the Lvy distribution are
discussed.Comment: 18pages, 7figures, using revtex4, to appear in Phys.Rev.
Desynchronization in diluted neural networks
The dynamical behaviour of a weakly diluted fully-inhibitory network of
pulse-coupled spiking neurons is investigated. Upon increasing the coupling
strength, a transition from regular to stochastic-like regime is observed. In
the weak-coupling phase, a periodic dynamics is rapidly approached, with all
neurons firing with the same rate and mutually phase-locked. The
strong-coupling phase is characterized by an irregular pattern, even though the
maximum Lyapunov exponent is negative. The paradox is solved by drawing an
analogy with the phenomenon of ``stable chaos'', i.e. by observing that the
stochastic-like behaviour is "limited" to a an exponentially long (with the
system size) transient. Remarkably, the transient dynamics turns out to be
stationary.Comment: 11 pages, 13 figures, submitted to Phys. Rev.
Noise Induced Complexity: From Subthreshold Oscillations to Spiking in Coupled Excitable Systems
We study stochastic dynamics of an ensemble of N globally coupled excitable
elements. Each element is modeled by a FitzHugh-Nagumo oscillator and is
disturbed by independent Gaussian noise. In simulations of the Langevin
dynamics we characterize the collective behavior of the ensemble in terms of
its mean field and show that with the increase of noise the mean field displays
a transition from a steady equilibrium to global oscillations and then, for
sufficiently large noise, back to another equilibrium. Diverse regimes of
collective dynamics ranging from periodic subthreshold oscillations to
large-amplitude oscillations and chaos are observed in the course of this
transition. In order to understand details and mechanisms of noise-induced
dynamics we consider a thermodynamic limit of the ensemble, and
derive the cumulant expansion describing temporal evolution of the mean field
fluctuations. In the Gaussian approximation this allows us to perform the
bifurcation analysis; its results are in good agreement with dynamical
scenarios observed in the stochastic simulations of large ensembles
Democratization in a passive dendritic tree : an analytical investigation
One way to achieve amplification of distal synaptic inputs on a dendritic tree is to scale the amplitude and/or duration of the synaptic conductance with its distance from the soma. This is an example of what is often referred to as “dendritic democracy”. Although well studied experimentally, to date this phenomenon has not been thoroughly explored from a mathematical perspective. In this paper we adopt a passive model of a dendritic tree with distributed excitatory synaptic conductances and analyze a number of key measures of democracy. In particular, via moment methods we derive laws for the transport, from synapse to soma, of strength, characteristic time, and dispersion. These laws lead immediately to synaptic scalings that overcome attenuation with distance. We follow this with a Neumann approximation of Green’s representation that readily produces the synaptic scaling that democratizes the peak somatic voltage response. Results are obtained for both idealized geometries and for the more realistic geometry of a rat CA1 pyramidal cell. For each measure of democratization we produce and contrast the synaptic scaling associated with treating the synapse as either a conductance change or a current injection. We find that our respective scalings agree up to a critical distance from the soma and we reveal how this critical distance decreases with decreasing branch radius
The spike train statistics for consonant and dissonant musical accords
The simple system composed of three neural-like noisy elements is considered.
Two of them (sensory neurons or sensors) are stimulated by noise and periodic
signals with different ratio of frequencies, and the third one (interneuron)
receives the output of these two sensors and noise. We propose the analytical
approach to analysis of Interspike Intervals (ISI) statistics of the spike
train generated by the interneuron. The ISI distributions of the sensory
neurons are considered to be known. The frequencies of the input sinusoidal
signals are in ratios, which are usual for music. We show that in the case of
small integer ratios (musical consonance) the input pair of sinusoids results
in the ISI distribution appropriate for more regular output spike train than in
a case of large integer ratios (musical dissonance) of input frequencies. These
effects are explained from the viewpoint of the proposed theory.Comment: 22 pages, 6 figure
Generalized Rate-Code Model for Neuron Ensembles with Finite Populations
We have proposed a generalized Langevin-type rate-code model subjected to
multiplicative noise, in order to study stationary and dynamical properties of
an ensemble containing {\it finite} neurons. Calculations using the
Fokker-Planck equation (FPE) have shown that owing to the multiplicative noise,
our rate model yields various kinds of stationary non-Gaussian distributions
such as gamma, inverse-Gaussian-like and log-normal-like distributions, which
have been experimentally observed. Dynamical properties of the rate model have
been studied with the use of the augmented moment method (AMM), which was
previously proposed by the author with a macroscopic point of view for
finite-unit stochastic systems. In the AMM, original -dimensional stochastic
differential equations (DEs) are transformed into three-dimensional
deterministic DEs for means and fluctuations of local and global variables.
Dynamical responses of the neuron ensemble to pulse and sinusoidal inputs
calculated by the AMM are in good agreement with those obtained by direct
simulation. The synchronization in the neuronal ensemble is discussed.
Variabilities of the firing rate and of the interspike interval (ISI) are shown
to increase with increasing the magnitude of multiplicative noise, which may be
a conceivable origin of the observed large variability in cortical neurons.Comment: 19 pages, 9 figures, accepted in Phys. Rev. E after minor
modification
First Passage Time Densities in Resonate-and-Fire Models
Motivated by the dynamics of resonant neurons we discuss the properties of
the first passage time (FPT) densities for nonmarkovian differentiable random
processes. We start from an exact expression for the FPT density in terms of an
infinite series of integrals over joint densities of level crossings, and
consider different approximations based on truncation or on approximate
summation of this series. Thus, the first few terms of the series give good
approximations for the FPT density on short times. For rapidly decaying
correlations the decoupling approximations perform well in the whole time
domain.
As an example we consider resonate-and-fire neurons representing stochastic
underdamped or moderately damped harmonic oscillators driven by white Gaussian
or by Ornstein-Uhlenbeck noise. We show, that approximations reproduce all
qualitatively different structures of the FPT densities: from monomodal to
multimodal densities with decaying peaks. The approximations work for the
systems of whatever dimension and are especially effective for the processes
with narrow spectral density, exactly when markovian approximations fail.Comment: 11 pages, 8 figure
Dynamical response of the Hodgkin-Huxley model in the high-input regime
The response of the Hodgkin-Huxley neuronal model subjected to stochastic
uncorrelated spike trains originating from a large number of inhibitory and
excitatory post-synaptic potentials is analyzed in detail. The model is
examined in its three fundamental dynamical regimes: silence, bistability and
repetitive firing. Its response is characterized in terms of statistical
indicators (interspike-interval distributions and their first moments) as well
as of dynamical indicators (autocorrelation functions and conditional
entropies). In the silent regime, the coexistence of two different coherence
resonances is revealed: one occurs at quite low noise and is related to the
stimulation of subthreshold oscillations around the rest state; the second one
(at intermediate noise variance) is associated with the regularization of the
sequence of spikes emitted by the neuron. Bistability in the low noise limit
can be interpreted in terms of jumping processes across barriers activated by
stochastic fluctuations. In the repetitive firing regime a maximization of
incoherence is observed at finite noise variance. Finally, the mechanisms
responsible for spike triggering in the various regimes are clearly identified.Comment: 14 pages, 24 figures in eps, submitted to Physical Review
First Passage Time Densities in Non-Markovian Models with Subthreshold Oscillations
Motivated by the dynamics of resonant neurons we consider a differentiable,
non-Markovian random process and particularly the time after which it
will reach a certain level . The probability density of this first passage
time is expressed as infinite series of integrals over joint probability
densities of and its velocity . Approximating higher order terms
of this series through the lower order ones leads to closed expressions in the
cases of vanishing and moderate correlations between subsequent crossings of
. For a linear oscillator driven by white or coloured Gaussian noise,
which models a resonant neuron, we show that these approximations reproduce the
complex structures of the first passage time densities characteristic for the
underdamped dynamics, where Markovian approximations (giving monotonous first
passage time distribution) fail
Stimulus - response curves of a neuronal model for noisy subthreshold oscillations and related spike generation
We investigate the stimulus-dependent tuning properties of a noisy ionic
conductance model for intrinsic subthreshold oscillations in membrane potential
and associated spike generation. On depolarization by an applied current, the
model exhibits subthreshold oscillatory activity with occasional spike
generation when oscillations reach the spike threshold. We consider how the
amount of applied current, the noise intensity, variation of maximum
conductance values and scaling to different temperature ranges alter the
responses of the model with respect to voltage traces, interspike intervals and
their statistics and the mean spike frequency curves. We demonstrate that
subthreshold oscillatory neurons in the presence of noise can sensitively and
also selectively be tuned by stimulus-dependent variation of model parameters.Comment: 19 pages, 7 figure
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