AIMC Topic: Nonlinear Dynamics

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An Efficient Population Density Method for Modeling Neural Networks with Synaptic Dynamics Manifesting Finite Relaxation Time and Short-Term Plasticity.

eNeuro
When incorporating more realistic synaptic dynamics, the computational efficiency of population density methods (PDMs) declines sharply due to the increase in the dimension of master equations. To avoid such a decline, we develop an efficient PDM, te...

A small-world topology enhances the echo state property and signal propagation in reservoir computing.

Neural networks : the official journal of the International Neural Network Society
Cortical neural connectivity has been shown to exhibit a small-world (SW) network topology. However, the role of the topology in neural information processing remains unclear. In this study, we investigated the learning performance of an echo state n...

Hindmarsh-Rose neuron model with memristors.

Bio Systems
We analyze single and coupled Hindmarsh-Rose neurons in the presence of a time varying electromagnetic field which results from the exchange of ions across the membrane. Memristors are used to model the relation between magnetic flux of the electroma...

Modelling the structure of object-independent human affordances of approaching to grasp for robotic hands.

PloS one
Grasp affordances in robotics represent different ways to grasp an object involving a variety of factors from vision to hand control. A model of grasp affordances that is able to scale across different objects, features and domains is needed to provi...

Response prediction of nonlinear hysteretic systems by deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Nonlinear hysteretic systems are common in many engineering problems. The maximum response estimation of a nonlinear hysteretic system under stochastic excitations is an important task for designing and maintaining such systems. Although a nonlinear ...

Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions ...

Fixed Points of Competitive Threshold-Linear Networks.

Neural computation
Threshold-linear networks (TLNs) are models of neural networks that consist of simple, perceptron-like neurons and exhibit nonlinear dynamics determined by the network's connectivity. The fixed points of a TLN, including both stable and unstable equi...

A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects.

Medical & biological engineering & computing
This paper presents a unified approach based on the recurrence quantification analysis (RQA) and approximate entropy (ApEn) for the classification of heart rate variability (HRV). In this paper, the optimum tolerance threshold (r) corresponding to Ap...

Structural, dynamical and symbolic observability: From dynamical systems to networks.

PloS one
Classical definitions of observability classify a system as either being observable or not. Observability has been recognized as an important feature to study complex networks, and as for dynamical systems the focus has been on determining conditions...

A neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints.

Neural networks : the official journal of the International Neural Network Society
This paper presents a neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints. The proposed neural network endows with a time-varying auxiliary function, which can guarantee that the state of th...