AIMC Topic: Computer Simulation

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Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks.

Chaos (Woodbury, N.Y.)
Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is s...

A universal concept based on cellular neural networks for ultrafast and flexible solving of differential equations.

IEEE transactions on neural networks and learning systems
This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stabilit...

Impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks.

IEEE transactions on neural networks and learning systems
This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functio...

Adaptive optimal control of highly dissipative nonlinear spatially distributed processes with neuro-dynamic programming.

IEEE transactions on neural networks and learning systems
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly ...

Quaternion-valued echo state networks.

IEEE transactions on neural networks and learning systems
Quaternion-valued echo state networks (QESNs) are introduced to cater for 3-D and 4-D processes, such as those observed in the context of renewable energy (3-D wind modeling) and human centered computing (3-D inertial body sensors). The introduction ...

Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle properties.

The Journal of toxicological sciences
An in silico method for predicting percutaneous absorption of cosmetic ingredients was developed by using artificial neural network (ANN) analysis to predict the human skin permeability coefficient (log Kp), taking account of the physicochemical prop...

In silico risk assessment for skin sensitization using artificial neural network analysis.

The Journal of toxicological sciences
The sensitizing potential of chemicals is usually identified and characterized using in vivo methods such as the murine local lymph node assay (LLNA). Due to regulatory constraints and ethical concerns, alternatives to animal testing are needed to pr...

Machine learning of single molecule free energy surfaces and the impact of chemistry and environment upon structure and dynamics.

The Journal of chemical physics
The conformational states explored by polymers and proteins can be controlled by environmental conditions (e.g., temperature, pressure, and solvent) and molecular chemistry (e.g., molecular weight and side chain identity). We introduce an approach em...