5,833 research outputs found
Classification and Geometry of General Perceptual Manifolds
Perceptual manifolds arise when a neural population responds to an ensemble
of sensory signals associated with different physical features (e.g.,
orientation, pose, scale, location, and intensity) of the same perceptual
object. Object recognition and discrimination requires classifying the
manifolds in a manner that is insensitive to variability within a manifold. How
neuronal systems give rise to invariant object classification and recognition
is a fundamental problem in brain theory as well as in machine learning. Here
we study the ability of a readout network to classify objects from their
perceptual manifold representations. We develop a statistical mechanical theory
for the linear classification of manifolds with arbitrary geometry revealing a
remarkable relation to the mathematics of conic decomposition. Novel
geometrical measures of manifold radius and manifold dimension are introduced
which can explain the classification capacity for manifolds of various
geometries. The general theory is demonstrated on a number of representative
manifolds, including L2 ellipsoids prototypical of strictly convex manifolds,
L1 balls representing polytopes consisting of finite sample points, and
orientation manifolds which arise from neurons tuned to respond to a continuous
angle variable, such as object orientation. The effects of label sparsity on
the classification capacity of manifolds are elucidated, revealing a scaling
relation between label sparsity and manifold radius. Theoretical predictions
are corroborated by numerical simulations using recently developed algorithms
to compute maximum margin solutions for manifold dichotomies. Our theory and
its extensions provide a powerful and rich framework for applying statistical
mechanics of linear classification to data arising from neuronal responses to
object stimuli, as well as to artificial deep networks trained for object
recognition tasks.Comment: 24 pages, 12 figures, Supplementary Material
Probing Split Supersymmetry with Cosmic Rays
A striking aspect of the recently proposed split supersymmetry is the
existence of heavy gluinos which are metastable because of the very heavy
squarks which mediate their decay. In this paper we correlate the expected flux
of these particles with the accompanying neutrino flux produced in inelastic
collisions in distant astrophysical sources. We show that an event rate at
the Pierre Auger Observatory of approximately 1 yr for gluino masses of
about 500 GeV is consistent with existing limits on neutrino fluxes. The
extremely low inelasticity of the gluino-containing hadrons in their collisions
with the air molecules makes possible a distinct characterization of the
showers induced in the atmosphere. Should such anomalous events be observed, we
show that their cosmogenic origin, in concert with the requirement that they
reach the Earth before decay, leads to a lower bound on their proper lifetime
of the order of 100 years, and consequently, to a lower bound on the scale of
supersymmetry breaking, GeV. Obtaining
such a bound is not possible in collider experiments.Comment: Version to be published in Phys. Rev.
On the accuracy of solving confluent Prony systems
In this paper we consider several nonlinear systems of algebraic equations
which can be called "Prony-type". These systems arise in various reconstruction
problems in several branches of theoretical and applied mathematics, such as
frequency estimation and nonlinear Fourier inversion. Consequently, the
question of stability of solution with respect to errors in the right-hand side
becomes critical for the success of any particular application. We investigate
the question of "maximal possible accuracy" of solving Prony-type systems,
putting stress on the "local" behavior which approximates situations with low
absolute measurement error. The accuracy estimates are formulated in very
simple geometric terms, shedding some light on the structure of the problem.
Numerical tests suggest that "global" solution techniques such as Prony's
algorithm and ESPRIT method are suboptimal when compared to this theoretical
"best local" behavior
Short-Term Memory in Orthogonal Neural Networks
We study the ability of linear recurrent networks obeying discrete time
dynamics to store long temporal sequences that are retrievable from the
instantaneous state of the network. We calculate this temporal memory capacity
for both distributed shift register and random orthogonal connectivity
matrices. We show that the memory capacity of these networks scales with system
size.Comment: 4 pages, 4 figures, to be published in Phys. Rev. Let
Swelling of particle-encapsulating random manifolds
We study the statistical mechanics of a closed random manifold of fixed area
and fluctuating volume, encapsulating a fixed number of noninteracting
particles. Scaling analysis yields a unified description of such swollen
manifolds, according to which the mean volume gradually increases with particle
number, following a single scaling law. This is markedly different from the
swelling under fixed pressure difference, where certain models exhibit
criticality. We thereby indicate when the swelling due to encapsulated
particles is thermodynamically inequivalent to that caused by fixed pressure.
The general predictions are supported by Monte Carlo simulations of two
particle-encapsulating model systems -- a two-dimensional self-avoiding ring
and a three-dimensional self-avoiding fluid vesicle. In the former the
particle-induced swelling is thermodynamically equivalent to the
pressure-induced one whereas in the latter it is not.Comment: 8 pages, 6 figure
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