2 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
An associative network with spatially organized connectivity
We investigate the properties of an autoassociative network of
threshold-linear units whose synaptic connectivity is spatially structured and
asymmetric. Since the methods of equilibrium statistical mechanics cannot be
applied to such a network due to the lack of a Hamiltonian, we approach the
problem through a signal-to-noise analysis, that we adapt to spatially
organized networks. The conditions are analyzed for the appearance of stable,
spatially non-uniform profiles of activity with large overlaps with one of the
stored patterns. It is also shown, with simulations and analytic results, that
the storage capacity does not decrease much when the connectivity of the
network becomes short range. In addition, the method used here enables us to
calculate exactly the storage capacity of a randomly connected network with
arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA