24,423 research outputs found
Sampling Properties of the Spectrum and Coherency of Sequences of Action Potentials
The spectrum and coherency are useful quantities for characterizing the
temporal correlations and functional relations within and between point
processes. This paper begins with a review of these quantities, their
interpretation and how they may be estimated. A discussion of how to assess the
statistical significance of features in these measures is included. In
addition, new work is presented which builds on the framework established in
the review section. This work investigates how the estimates and their error
bars are modified by finite sample sizes. Finite sample corrections are derived
based on a doubly stochastic inhomogeneous Poisson process model in which the
rate functions are drawn from a low variance Gaussian process. It is found
that, in contrast to continuous processes, the variance of the estimators
cannot be reduced by smoothing beyond a scale which is set by the number of
point events in the interval. Alternatively, the degrees of freedom of the
estimators can be thought of as bounded from above by the expected number of
point events in the interval. Further new work describing and illustrating a
method for detecting the presence of a line in a point process spectrum is also
presented, corresponding to the detection of a periodic modulation of the
underlying rate. This work demonstrates that a known statistical test,
applicable to continuous processes, applies, with little modification, to point
process spectra, and is of utility in studying a point process driven by a
continuous stimulus. While the material discussed is of general applicability
to point processes attention will be confined to sequences of neuronal action
potentials (spike trains) which were the motivation for this work.Comment: 33 pages, 9 figure
Effective phase description of noise-perturbed and noise-induced oscillations
An effective description of a general class of stochastic phase oscillators
is presented. For this, the effective phase velocity is defined either by
invariant probability density or via first passage times. While the first
approach exhibits correct frequency and distribution density, the second one
yields proper phase resetting curves. Their discrepancy is most pronounced for
noise-induced oscillations and is related to non-monotonicity of the phase
fluctuations
Stochastic Effects in Physical Systems
A tutorial review is given of some developments and applications of
stochastic processes from the point of view of the practicioner physicist. The
index is the following: 1.- Introduction 2.- Stochastic Processes 3.- Transient
Stochastic Dynamics 4.- Noise in Dynamical Systems 5.- Noise Effects in
Spatially Extended Systems 6.- Fluctuations, Phase Transitions and
Noise-Induced Transitions.Comment: 93 pages, 36 figures, LaTeX. To appear in Instabilities and
Nonequilibrium Structures VI, E. Tirapegui and W. Zeller,eds. Kluwer Academi
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.
A machine learning framework for data driven acceleration of computations of differential equations
We propose a machine learning framework to accelerate numerical computations
of time-dependent ODEs and PDEs. Our method is based on recasting
(generalizations of) existing numerical methods as artificial neural networks,
with a set of trainable parameters. These parameters are determined in an
offline training process by (approximately) minimizing suitable (possibly
non-convex) loss functions by (stochastic) gradient descent methods. The
proposed algorithm is designed to be always consistent with the underlying
differential equation. Numerical experiments involving both linear and
non-linear ODE and PDE model problems demonstrate a significant gain in
computational efficiency over standard numerical methods
Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics
Linearized catalytic reaction equations modeling e.g. the dynamics of genetic
regulatory networks under the constraint that expression levels, i.e. molecular
concentrations of nucleic material are positive, exhibit nontrivial dynamical
properties, which depend on the average connectivity of the reaction network.
In these systems the inflation of the edge of chaos and multi-stability have
been demonstrated to exist. The positivity constraint introduces a nonlinearity
which makes chaotic dynamics possible. Despite the simplicity of such minimally
nonlinear systems, their basic properties allow to understand fundamental
dynamical properties of complex biological reaction networks. We analyze the
Lyapunov spectrum, determine the probability to find stationary oscillating
solutions, demonstrate the effect of the nonlinearity on the effective in- and
out-degree of the active interaction network and study how the frequency
distributions of oscillatory modes of such system depend on the average
connectivity.Comment: 11 pages, 5 figure
Phase transition in protocols minimizing work fluctuations
For two canonical examples of driven mesoscopic systems - a
harmonically-trapped Brownian particle and a quantum dot - we numerically
determine the finite-time protocols that optimize the compromise between the
standard deviation and the mean of the dissipated work. In the case of the
oscillator, we observe a collection of protocols that smoothly trade-off
between average work and its fluctuations. However, for the quantum dot, we
find that as we shift the weight of our optimization objective from average
work to work standard deviation, there is an analog of a first-order phase
transition in protocol space: two distinct protocols exchange global optimality
with mixed protocols akin to phase coexistence. As a result, the two types of
protocols possess qualitatively different properties and remain distinct even
in the infinite duration limit: optimal-work-fluctuation protocols never
coalesce with the minimal work protocols, which therefore never become
quasistatic.Comment: 6 pages, 6 figures + SI as ancillary fil
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