1,934 research outputs found
The path-integral analysis of an associative memory model storing an infinite number of finite limit cycles
It is shown that an exact solution of the transient dynamics of an
associative memory model storing an infinite number of limit cycles with l
finite steps by means of the path-integral analysis. Assuming the Maxwell
construction ansatz, we have succeeded in deriving the stationary state
equations of the order parameters from the macroscopic recursive equations with
respect to the finite-step sequence processing model which has retarded
self-interactions. We have also derived the stationary state equations by means
of the signal-to-noise analysis (SCSNA). The signal-to-noise analysis must
assume that crosstalk noise of an input to spins obeys a Gaussian distribution.
On the other hand, the path-integral method does not require such a Gaussian
approximation of crosstalk noise. We have found that both the signal-to-noise
analysis and the path-integral analysis give the completely same result with
respect to the stationary state in the case where the dynamics is
deterministic, when we assume the Maxwell construction ansatz.
We have shown the dependence of storage capacity (alpha_c) on the number of
patterns per one limit cycle (l). Storage capacity monotonously increases with
the number of steps, and converges to alpha_c=0.269 at l ~= 10. The original
properties of the finite-step sequence processing model appear as long as the
number of steps of the limit cycle has order l=O(1).Comment: 24 pages, 3 figure
Generating functional analysis of CDMA detection dynamics
We investigate the detection dynamics of the parallel interference canceller
(PIC) for code-division multiple-access (CDMA) multiuser detection, applied to
a randomly spread, fully syncronous base-band uncoded CDMA channel model with
additive white Gaussian noise (AWGN) under perfect power control in the
large-system limit. It is known that the predictions of the density evolution
(DE) can fairly explain the detection dynamics only in the case where the
detection dynamics converge. At transients, though, the predictions of DE
systematically deviate from computer simulation results. Furthermore, when the
detection dynamics fail to convergence, the deviation of the predictions of DE
from the results of numerical experiments becomes large. As an alternative,
generating functional analysis (GFA) can take into account the effect of the
Onsager reaction term exactly and does not need the Gaussian assumption of the
local field. We present GFA to evaluate the detection dynamics of PIC for CDMA
multiuser detection. The predictions of GFA exhibits good consistency with the
computer simulation result for any condition, even if the dynamics fail to
convergence.Comment: 14 pages, 3 figure
Generating functional analysis of complex formation and dissociation in large protein interaction networks
We analyze large systems of interacting proteins, using techniques from the
non-equilibrium statistical mechanics of disordered many-particle systems.
Apart from protein production and removal, the most relevant microscopic
processes in the proteome are complex formation and dissociation, and the
microscopic degrees of freedom are the evolving concentrations of unbound
proteins (in multiple post-translational states) and of protein complexes. Here
we only include dimer-complexes, for mathematical simplicity, and we draw the
network that describes which proteins are reaction partners from an ensemble of
random graphs with an arbitrary degree distribution. We show how generating
functional analysis methods can be used successfully to derive closed equations
for dynamical order parameters, representing an exact macroscopic description
of the complex formation and dissociation dynamics in the infinite system
limit. We end this paper with a discussion of the possible routes towards
solving the nontrivial order parameter equations, either exactly (in specific
limits) or approximately.Comment: 14 pages, to be published in Proc of IW-SMI-2009 in Kyoto (Journal of
Phys Conference Series
Linear Complexity Lossy Compressor for Binary Redundant Memoryless Sources
A lossy compression algorithm for binary redundant memoryless sources is
presented. The proposed scheme is based on sparse graph codes. By introducing a
nonlinear function, redundant memoryless sequences can be compressed. We
propose a linear complexity compressor based on the extended belief
propagation, into which an inertia term is heuristically introduced, and show
that it has near-optimal performance for moderate block lengths.Comment: 4 pages, 1 figur
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