8,745 research outputs found
Decorrelation of neural-network activity by inhibitory feedback
Correlations in spike-train ensembles can seriously impair the encoding of
information by their spatio-temporal structure. An inevitable source of
correlation in finite neural networks is common presynaptic input to pairs of
neurons. Recent theoretical and experimental studies demonstrate that spike
correlations in recurrent neural networks are considerably smaller than
expected based on the amount of shared presynaptic input. By means of a linear
network model and simulations of networks of leaky integrate-and-fire neurons,
we show that shared-input correlations are efficiently suppressed by inhibitory
feedback. To elucidate the effect of feedback, we compare the responses of the
intact recurrent network and systems where the statistics of the feedback
channel is perturbed. The suppression of spike-train correlations and
population-rate fluctuations by inhibitory feedback can be observed both in
purely inhibitory and in excitatory-inhibitory networks. The effect is fully
understood by a linear theory and becomes already apparent at the macroscopic
level of the population averaged activity. At the microscopic level,
shared-input correlations are suppressed by spike-train correlations: In purely
inhibitory networks, they are canceled by negative spike-train correlations. In
excitatory-inhibitory networks, spike-train correlations are typically
positive. Here, the suppression of input correlations is not a result of the
mere existence of correlations between excitatory (E) and inhibitory (I)
neurons, but a consequence of a particular structure of correlations among the
three possible pairings (EE, EI, II)
Analysis of two-point statistics of cosmic shear: III. Covariances of shear measures made easy
In recent years cosmic shear, the weak gravitational lensing effect by the
large-scale structure of the Universe, has proven to be one of the
observational pillars on which the cosmological concordance model is founded.
Several cosmic shear statistics have been developed in order to analyze data
from surveys. For the covariances of the prevalent second-order measures we
present simple and handy formulae, valid under the assumptions of Gaussian
density fluctuations and a simple survey geometry. We also formulate these
results in the context of shear tomography, i.e. the inclusion of redshift
information, and generalize them to arbitrary data field geometries. We define
estimators for the E- and B-mode projected power spectra and show them to be
unbiased in the case of Gaussianity and a simple survey geometry. From the
covariance of these estimators we demonstrate how to derive covariances of
arbitrary combinations of second-order cosmic shear measures. We then
recalculate the power spectrum covariance for general survey geometries and
examine the bias thereby introduced on the estimators for exemplary
configurations. Our results for the covariances are considerably simpler than
and analytically shown to be equivalent to the real-space approach presented in
the first paper of this series. We find good agreement with other numerical
evaluations and confirm the general properties of the covariance matrices. The
studies of the specific survey configurations suggest that our simplified
covariances may be employed for realistic survey geometries to good
approximation.Comment: 15 pages, including 4 figures (Fig. 3 reduced in quality); minor
changes, Fig. 4 extended; published in A&
The azimuth structure of nuclear collisions -- I
We describe azimuth structure commonly associated with elliptic and directed
flow in the context of 2D angular autocorrelations for the purpose of precise
separation of so-called nonflow (mainly minijets) from flow. We extend the
Fourier-transform description of azimuth structure to include power spectra and
autocorrelations related by the Wiener-Khintchine theorem. We analyze several
examples of conventional flow analysis in that context and question the
relevance of reaction plane estimation to flow analysis. We introduce the 2D
angular autocorrelation with examples from data analysis and describe a
simulation exercise which demonstrates precise separation of flow and nonflow
using the 2D autocorrelation method. We show that an alternative correlation
measure based on Pearson's normalized covariance provides a more intuitive
measure of azimuth structure.Comment: 27 pages, 12 figure
Effect of Ground Motion Characteristics on the Seismic Response of Torsionally Coupled Elastic Systems
This study presents a systematic investigation of the effects of ground motion
characteristics, especially its multi-directional character, on the response of
torsionally coupled elastic structural systems. The ground motion model is probabilistic
and is founded on the assumption of the existence of ground motion principal directions.
The structural systems considered are single-story and multi-story elastic shear beam
models with stiffness eccentricity.National Science Foundation Grants ENV 77-07190 and PFR 80-0258
A unified view on weakly correlated recurrent networks
The diversity of neuron models used in contemporary theoretical neuroscience
to investigate specific properties of covariances raises the question how these
models relate to each other. In particular it is hard to distinguish between
generic properties and peculiarities due to the abstracted model. Here we
present a unified view on pairwise covariances in recurrent networks in the
irregular regime. We consider the binary neuron model, the leaky
integrate-and-fire model, and the Hawkes process. We show that linear
approximation maps each of these models to either of two classes of linear rate
models, including the Ornstein-Uhlenbeck process as a special case. The classes
differ in the location of additive noise in the rate dynamics, which is on the
output side for spiking models and on the input side for the binary model. Both
classes allow closed form solutions for the covariance. For output noise it
separates into an echo term and a term due to correlated input. The unified
framework enables us to transfer results between models. For example, we
generalize the binary model and the Hawkes process to the presence of
conduction delays and simplify derivations for established results. Our
approach is applicable to general network structures and suitable for
population averages. The derived averages are exact for fixed out-degree
network architectures and approximate for fixed in-degree. We demonstrate how
taking into account fluctuations in the linearization procedure increases the
accuracy of the effective theory and we explain the class dependent differences
between covariances in the time and the frequency domain. Finally we show that
the oscillatory instability emerging in networks of integrate-and-fire models
with delayed inhibitory feedback is a model-invariant feature: the same
structure of poles in the complex frequency plane determines the population
power spectra
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