Hopfield neural network model for explaining double dissociation in semantic memory impairment

Re-Evaluation of Attractor Neural Network Model to Explain Double Dissociation in Semantic Memory Disorder

Structure of semantic memory was investigated in the way of neural network simulations in detail. In the literature, it is well-known that brain damaged patients often showed category specific disorder in various cognitive neuropsychological tasks like picture naming, categorisation, identification tasks and so on. In order to describe semantic memory disorder of brain damaged patients, the att...

A Hopfield Neural Network Model for the Outerplanar Drawing Problem

In the outerplanar (other alternate concepts are circular or one-page) drawing, one places vertices of a n−vertex m−edge connected graph G along a circle, and the edges are drawn as straight lines. The minimal number of crossings over all outerplanar drawings of the graph G is called the outerplanar (circular, convex, or one-page) crossing number of the graph G. To find a drawing achieving the ...

A Hopfield-Type Neural Network Based Model for Temporal Constraints

In this paper we present an approximation method based on discrete Hopfield neural network (DHNN) for solving temporal constraint satisfaction problems. This method is of interest for problems involving numeric and symbolic temporal constraints and where a solution satisfying the constraints of the problem needs to be found within a given deadline. More precisely the method has the ability to p...

Evidence of Double Dissociation in Auditory Agnosia: Implications for Rehabilition

Hopfield Neural Network for UWB Multiuser Detection

-The Hopfield neural network (HNN) is introduced in the paper and is proposed as an effective multiuser detection in direct sequence-ultra-wideband (DS-UWB) systems. It can approximate to maximum likelihood (ML) detector by mathematical analysis. According to the HNN-based technique, the computer simulation results show that they have good performances and much lower computational complexity in...