activity
19982011
most citedMean-field dynamics of sequence processing neural networks with finite connectivity

11 citations · 43 across the 10 of their papers we have counts for

collaborators

10 papers

cond-mat.stat-mech2011★ 11 cited

Phase transitions in the three-state Ising spin-glass model with finite connectivity

R. Erichsen, W. K. Theumann

The statistical mechanics of a two-state Ising spin-glass model with finite random connectivity, in which each site is connected to a finite number of other sites, is extended in t…

cond-mat.dis-nn2008★ 5 cited

Instability of frozen-in states in synchronous Hebbian neural networks

F. L. Metz, W. K. Theumann

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 determin…

cond-mat.dis-nn2005★ 2 cited

Feed-forward chains of recurrent attractor neural networks with finite dilution near saturation

F. L. Metz, W. K. Theumann

A stationary state replica analysis for a dual neural network model that interpolates between a fully recurrent symmetric attractor network and a strictly feed-forward layered netw…

cond-mat.dis-nn2005★ 2 cited

Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation

F. L. Metz, W. K. Theumann

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 la…

cond-mat.dis-nn2004★ 2 cited

The three-state layered neural network with finite dilution

W. K. Theumann, R. Erichsen

The dynamics and the stationary states of an exactly solvable three-state layered feed-forward neural network model with asymmetric synaptic connections, finite dilution and low pa…

cond-mat.dis-nn2003★ 2 cited

A layered neural network with three-state neurons optimizing the mutual information

D. Bolle, R. Erichsen, W. K. Theumann

The time evolution of an exactly solvable layered feedforward neural network with three-state neurons and optimizing the mutual information is studied for arbitrary synaptic noise…