Biomedical subjects

A Meyer-Bäse

Publications and source records attributed to A Meyer-Bäse.

4 recordsLinked to original sources

PubMed2000

An interspike interval method for computing phase locking from neural firing.

Inner hair cells (IHCs) transform the mechanical movements of the basilar membrane into electrical impulses. The impulse coding of the IHCs is the main information carrier in the auditory process and is the basis for improvements of cochlea implants as well as for low rate, high-quality speech processing and compression. This paper shows how to compute the speech signal from the neural firing based on the analysis of the interspike interval histogram. This new approach solves problems that other standard analysis methods do not solve sufficiently well.

Action Potentials↗

PubMed1999

Asymptotic hyperstability of a class of neural networks.

This paper is concerned with the asymptotic hyperstability of recurrent neural networks. We derive based on the stability results necessary and sufficient conditions for the network parameters. The results we achieve are more general than those based on Lyapunov methods, since they provide milder constraints on the connection weights than the conventional results and do not suppose symmetry of the weights.

Neural Networks, Computer↗

PubMed1997

Stability analysis of a class of noise perturbed neural networks.

We establish robustness stability results for a specific type of artificial neural networks for associative memories under parameter perturbations and determine conditions that ensure the existence of asymptotically stable equilibria of the perturbed neural system that are the asymptotically stable equilibria of the original unperturbed neural network. The proposed stability analysis tool is the sliding mode control and it facilitates the analysis by considering only a reduced-order system instead of the original one and time-dependent external stimuli.

Association Learning↗

PubMed1996

Singular perturbation analysis of competitive neural networks with different time scales.

The dynamics of complex neural networks must include the aspects of long- and short-term memory. The behavior of the network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. The main idea of this paper is to apply a stability analysis method of fixed points of the combined activity and weight dynamics for a special class of competitive neural networks. We present a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables as a global stability method and a modality of detecting the local stability behavior around individual equilibrium points.

Algorithms↗