ADAPTIVE THRESHOLDS FOR NEURAL NETWORKS WITH SYNAPTIC NOISE

Adaptive Thresholds for Layered Neural Networks with Synaptic Noise

The inclusion of a macroscopic adaptive threshold is studied for the retrieval dynamics of layered feedforward neural network models with synaptic noise. It is shown that if the threshold is chosen appropriately as a function of the cross-talk noise and of the activity of the stored patterns, adapting itself automatically in the course of the recall process, an autonomous functioning of the net...

Adaptive Thresholds for Neural Networks with Synaptic Noise

The inclusion of a macroscopic adaptive threshold is studied for the retrieval dynamics of both layered feedforward and fully connected neural network models with synaptic noise. These two types of architectures require a different method to be solved numerically. In both cases it is shown that, if the threshold is chosen appropriately as a function of the cross-talk noise and of the activity o...

Signal Clustering Using Self-Organizing Neural Networks with Adaptive Thresholds

This paper presents an adaptive signal clustering technique using a self-organizing neural network (NN) algorithm, Dignet. The neural network Dignet performs self-organized clustering on input patterns without supervised learning procedures. The number of clusters is automatically generated by adaptive learning and convergence of parameters on the network. The initial threshold value used in Di...

On-off intermittency in small neural networks with synaptic noise

Neural networks with dynamical thresholds.

We incorporate local threshold functions into the dynamics of the Hopfield model. These functions depend on the history of the individual spin (= neuron). They reach a maximal height if the spin remains constant. The resulting one-pattern model has ferromagnetic, paramagnetic, and periodic phases. This model is solved by a master equation and approximated by simplified systems of equations that...