55 research outputs found
Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation
The dynamics and the stationary states for the competition between pattern
reconstruction and asymmetric sequence processing are studied here in an
exactly solvable feed-forward layered neural network model of binary units and
patterns near saturation. Earlier work by Coolen and Sherrington on a parallel
dynamics far from saturation is extended here to account for finite stochastic
noise due to a Hebbian and a sequential learning rule. Phase diagrams are
obtained with stationary states and quasi-periodic non-stationary solutions.
The relevant dependence of these diagrams and of the quasi-periodic solutions
on the stochastic noise and on initial inputs for the overlaps is explicitly
discussed.Comment: 9 pages, 7 figure
Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing
The effects of dominant sequential interactions are investigated in an
exactly solvable feed-forward layered neural network model of binary units and
patterns near saturation in which the interaction consists of a Hebbian part
and a symmetric sequential term. Phase diagrams of stationary states are
obtained and a new phase of cyclic correlated states of period two is found for
a weak Hebbian term, independently of the number of condensed patterns .Comment: 8 pages and 5 figure
Instability of frozen-in states in synchronous Hebbian neural networks
The full dynamics of a synchronous recurrent neural network model with Ising
binary units and a Hebbian learning rule with a finite self-interaction is
studied in order to determine the stability to synaptic and stochastic noise of
frozen-in states that appear in the absence of both kinds of noise. Both, the
numerical simulation procedure of Eissfeller and Opper and a new alternative
procedure that allows to follow the dynamics over larger time scales have been
used in this work. It is shown that synaptic noise destabilizes the frozen-in
states and yields either retrieval or paramagnetic states for not too large
stochastic noise. The indications are that the same results may follow in the
absence of synaptic noise, for low stochastic noise.Comment: 14 pages and 4 figures; accepted for publication in J. Phys. A: Math.
Ge
Time evolution of the extremely diluted Blume-Emery-Griffiths neural network
The time evolution of the extremely diluted Blume-Emery-Griffiths neural
network model is studied, and a detailed equilibrium phase diagram is obtained
exhibiting pattern retrieval, fluctuation retrieval and self-sustained activity
phases. It is shown that saddle-point solutions associated with fluctuation
overlaps slow down considerably the flow of the network states towards the
retrieval fixed points. A comparison of the performance with other three-state
networks is also presented.Comment: 8 pages, 5 figure
Quantum Critical Point in the Spin Glass-Kondo Transition in Heavy Fermion Systems
The Kondo-Spin Glass competition is studied in a theoretical model of a Kondo
lattice with an intra-site Kondo type exchange interaction treated within the
mean field approximation, an inter-site quantum Ising exchange interaction with
random couplings among localized spins and an additional transverse field in
the x direction, which represents a simple quantum mechanism of spin flipping.
We obtain two second order transition lines from the spin-glass state to the
paramagnetic one and then to the Kondo state. For a reasonable set of the
different parameters, the two second order transition lines do not intersect
and end in two distinct QCP.Comment: 20 pages; 1 figure; to appear in Physical Review
Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics
The synchronous dynamics and the stationary states of a recurrent attractor
neural network model with competing synapses between symmetric sequence
processing and Hebbian pattern reconstruction is studied in this work allowing
for the presence of a self-interaction for each unit. Phase diagrams of
stationary states are obtained exhibiting phases of retrieval, symmetric and
period-two cyclic states as well as correlated and frozen-in states, in the
absence of noise. The frozen-in states are destabilised by synaptic noise and
well separated regions of correlated and cyclic states are obtained. Excitatory
or inhibitory self-interactions yield enlarged phases of fixed-point or cyclic
behaviour.Comment: Accepted for publication in Journal of Physics A: Mathematical and
Theoretica
First-order transition in the one-dimensional three-state Potts model with long-range interactions
The first-order phase transition in the three-state Potts model with
long-range interactions decaying as has been examined by
numerical simulations using recently proposed Luijten-Bl\"ote algorithm. By
applying scaling arguments to the interface free energy, the Binder's
fourth-order cumulant, and the specific heat maximum, the change in the
character of the transition through variation of parameter was
studied.Comment: 6 pages (containing 5 figures), to appear in Phys. Rev.
Optimally adapted multi-state neural networks trained with noise
The principle of adaptation in a noisy retrieval environment is extended here
to a diluted attractor neural network of Q-state neurons trained with noisy
data. The network is adapted to an appropriate noisy training overlap and
training activity which are determined self-consistently by the optimized
retrieval attractor overlap and activity. The optimized storage capacity and
the corresponding retriever overlap are considerably enhanced by an adequate
threshold in the states. Explicit results for improved optimal performance and
new retriever phase diagrams are obtained for Q=3 and Q=4, with coexisting
phases over a wide range of thresholds. Most of the interesting results are
stable to replica-symmetry-breaking fluctuations.Comment: 22 pages, 5 figures, accepted for publication in PR
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