Modeling somatic computation with non-neural bioelectric networks

Integrating evolutionary computation with neural networks

There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited 10 deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniqucs. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents ;I solution of a classi...

Turing Computation with Recurrent Artificial Neural Networks

We improve the results by Siegelmann & Sontag [1, 2] by providing a novel and parsimonious constructive mapping between Turing Machines and Recurrent Artificial Neural Networks, based on recent developments of Nonlinear Dynamical Automata. The architecture of the resulting R-ANNs is simple and elegant, stemming from its transparent relation with the underlying NDAs. These characteristics yield ...

Modeling with Spiking Neural Networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific applicat...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)

Analog Computation Via Neural Networks

We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of “neurons”. If allowed exponential time for computation, they turn out to have unbounded power. However, under polynomial-time constraints there are limits on their capabilit...