Stability of Localized Patterns in Neural Fields

Dynamic Approximation of Spatiotemporal Receptive Fields in Nonlinear Neural Field Models

This article presents an approximation method to reduce the spatiotemporal behavior of localized activation peaks (also called "bumps") in non-linear neural field equations to a set of coupled ordinary differential equations (ODEs) for only the amplitudes and tuning widths of these peaks. This enables a simplified analysis of steady-state receptive fields and their stability, as well as spatiot...

Increasing Autonomy of Learning Sensorimotor Transformations with Dynamic Neural Fields

In this paper, we introduce a neuraldynamic architecture that enables autonomous learning of sensory-motor mappings in a closed behavioral loop. Dynamic neural fields ensure stability of perceptual and motor representations, a neural-dynamic representation of the condition-of-satisfaction autonomously terminates the current action and enables activation of the next action, triggering a transien...

Vector Fields and Neural Networks

We consider neural network models described by systems of (continuous time) differential equations. Th e dynamical nature of each model is identified, symmet ric networks being relat ed to gradient vector fields and asymmet ric networks decomposed into their gradient and Hamilt onian components . From thi s identification follows, in particular , a simple characterizat ion of st ructural stabil...

Drilling Stuck Pipe Prediction in Iranian Oil Fields: An Artificial Neural Network Approach

Neural network model with discrete and continuous information representation

An associative memory model and a neural network model with a Mexican-hat type interaction are the two most typical attractor networks used in the artificial neural network models. The associative memory model has discretely distributed fixed-point attractors, and achieves a discrete information representation. On the other hand, a neural network model with a Mexican-hat type interaction uses a...