1,504 research outputs found
Distribution of Mutual Information
The mutual information of two random variables i and j with joint
probabilities t_ij is commonly used in learning Bayesian nets as well as in
many other fields. The chances t_ij are usually estimated by the empirical
sampling frequency n_ij/n leading to a point estimate I(n_ij/n) for the mutual
information. To answer questions like "is I(n_ij/n) consistent with zero?" or
"what is the probability that the true mutual information is much larger than
the point estimate?" one has to go beyond the point estimate. In the Bayesian
framework one can answer these questions by utilizing a (second order) prior
distribution p(t) comprising prior information about t. From the prior p(t) one
can compute the posterior p(t|n), from which the distribution p(I|n) of the
mutual information can be calculated. We derive reliable and quickly computable
approximations for p(I|n). We concentrate on the mean, variance, skewness, and
kurtosis, and non-informative priors. For the mean we also give an exact
expression. Numerical issues and the range of validity are discussed.Comment: 8 page
Winnerless competition between sensory neurons generates chaos: A possible mechanism for molluscan hunting behavior
© 2002 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.In the presence of prey, the marine mollusk Clione limacina exhibits search behavior, i.e., circular motions whose plane and radius change in a chaotic-like manner. We have formulated a dynamical model of the chaotic hunting behavior of Clione based on physiological in vivo and in vitroexperiments. The model includes a description of the action of the cerebral hunting interneuron on the receptor neurons of the gravity sensory organ, the statocyst. A network of six receptor model neurons with Lotka–Volterra-type dynamics and nonsymmetric inhibitory interactions has no simple static attractors that correspond to winner take all phenomena. Instead, the winnerless competition induced by the hunting neuron displays hyperchaos with two positive Lyapunov exponents. The origin of the chaos is related to the interaction of two clusters of receptor neurons that are described with two heteroclinic loops in phase space. We hypothesize that the chaotic activity of the receptor neurons can drive the complex behavior of Clione observed during hunting.Support for this work came from NIH Grant No. 2R01 NS38022- 05A1. P.V. acknowledges support from MCT BFI2000-0157. M.R. acknowledges support from U.S. Department of Energy Grant No. DE-FG03-96ER14592
Liberty, Equality and Not Too Much Fraternity: An Experience in Practical Application of Liberal Education Teaching Techniques
The paper explores an experience in practical application of liberal education teaching techniques. We describe the most frequently used techniques and present sample classroom assignments based on this framework. We also discuss the opportunities and limitations provided by the use of these methods in a classroom setting.
Keywords: teaching techniques, liberal education, writing and analytical reading, humanities teaching
Generation linewidth of an auto-oscillator with a nonlinear frequency shift: Spin-torque nano-oscillator
It is shown that the generation linewidth of an auto-oscillator with a
nonlinear frequency shift (i.e. an auto-oscillator in which frequency depends
on the oscillation amplitude) is substantially larger than the linewidth of a
conventional quasi-linear auto-oscillator due to the renormalization of the
phase noise caused by the nonlinearity of the oscillation frequency. The
developed theory, when applied to a spin-torque nano-contact auto-oscillator,
predicts a minimum of the generation linewidth when the nano-contact is
magnetized at a critical angle to its plane, corresponding to the minimum
nonlinear frequency shift, in good agreement with recent experiments.Comment: 4 pages, 2 figure
Dynamical Encoding by Networks of Competing Neuron Groups: Winnerless Competition
Following studies of olfactory processing in insects and fish, we investigate neural networks whose dynamics in phase space is represented by orbits near the heteroclinic connections between saddle regions (fixed points or limit cycles). These networks encode input information as trajectories along the heteroclinic connections. If there are N neurons in the network, the capacity is approximately e(N-1)!, i.e., much larger than that of most traditional network structures. We show that a small winnerless competition network composed of FitzHugh-Nagumo spiking neurons efficiently transforms input information into a spatiotemporal output
Essential spectra and exponential estimates of eigenfunctions of lattice operators of quantum mechanics
This paper is devoted to estimates of the exponential decay of eigenfunctions
of difference operators on the lattice Z^n which are discrete analogs of the
Schr\"{o}dinger, Dirac and square-root Klein-Gordon operators. Our
investigation of the essential spectra and the exponential decay of
eigenfunctions of the discrete spectra is based on the calculus of so-called
pseudodifference operators (i.e., pseudodifferential operators on the group
Z^n) with analytic symbols and on the limit operators method. We obtain a
description of the location of the essential spectra and estimates of the
eigenfunctions of the discrete spectra of the main lattice operators of quantum
mechanics, namely: matrix Schr\"{o}dinger operators on Z^n, Dirac operators on
Z^3, and square root Klein-Gordon operators on Z^n
Nonlinear dynamics in one dimension: On a criterion for coarsening and its temporal law
We develop a general criterion about coarsening for a class of nonlinear
evolution equations describing one dimensional pattern-forming systems. This
criterion allows one to discriminate between the situation where a coarsening
process takes place and the one where the wavelength is fixed in the course of
time. An intermediate scenario may occur, namely `interrupted coarsening'. The
power of the criterion lies in the fact that the statement about the occurrence
of coarsening, or selection of a length scale, can be made by only inspecting
the behavior of the branch of steady state periodic solutions. The criterion
states that coarsening occurs if lambda'(A)>0 while a length scale selection
prevails if lambda'(A)<0, where is the wavelength of the pattern and A
is the amplitude of the profile. This criterion is established thanks to the
analysis of the phase diffusion equation of the pattern. We connect the phase
diffusion coefficient D(lambda) (which carries a kinetic information) to
lambda'(A), which refers to a pure steady state property. The relationship
between kinetics and the behavior of the branch of steady state solutions is
established fully analytically for several classes of equations. Another
important and new result which emerges here is that the exploitation of the
phase diffusion coefficient enables us to determine in a rather straightforward
manner the dynamical coarsening exponent. Our calculation, based on the idea
that |D(lambda)|=lambda^2/t, is exemplified on several nonlinear equations,
showing that the exact exponent is captured. Some speculations about the
extension of the present results to higher dimension are outlined.Comment: 16 pages. Only a few minor changes. Accepted for publication in
Physical Review
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