Connectionist models, also known as Parallel Distributed Processing (PDP) models, are a class of computational models often used to model aspects of human perception, cognition, and behaviour, the learning processes underlying such behaviour, and the storage and retrieval of information from memory

The DEVS formalism supports modeling of discrete event systems in a hierarchical, modular manner based on the ob-ject-oriented world view. System modeling requires not only understanding of modeling framework but also domain kno-wledge of the system. Therefore, successful modeling may need a means

Many researchers interested in connectionist models accept that such models are "neurally inspired " but do not worry too much about whether their models are biologically realistic. While such a position may be perfectly justifiable, the present paper attempts to illustrate how

How have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical

synapses change a little on each reinstatement, and that remote memory is based on accumulated neocortical changes. Models that learn via changes to connections help explain this organization. These models discover the structure in ensembles of items if learning of each item is gradual and interleaved

Previous work has shown how a back-propagation network with recurrent connections can successfully model many aspects of human spoken word recogni-tion (Norris, 1988, 1990, 1992, 1993). However, such networks are unable to revise their decisions in the light of subsequent context. TRACE (Mc

based upon graded, malleable, distributed representations, connectionist modeling has reopened the question of what could be learned from the environment in the absence of detailed innate knowledge. This paper provides an overview of connectionist models of language processing, at both the lexical

Connectionist models have made significant contributions to understanding developmental phenomena, mainly by providing novel computational accounts of behavioral emergence and change. What is the fate of such models given the increasing interest in and information about the biological bases

Introduction The prototypical use of "classical" connectionist models (including the multilayer perceptron (MLP), the Hopfield network and the Kohonen self-organizing map) concerns static data processing. These classical models are not well suited to working with data varying over time

The prototypical use of “classical ” connectionist models (including the multilayer perceptron (MLP), the Hopfield network and the Kohonen self-organizing map) concerns static data processing. These classical models are not well suited to working with data varying over time. In response to this