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Saturday, December 27, 2014

Parallel Processing: The Connectionist Model

Parallel Processing: The Connectionist Model
Computer-inspired information-processing theories assume that humans, like computers, process information serially. That is, information is processed one step after another. These aspects involve parallel processing, in which multiple operations go on all at once. Therefore, the distribution of parallel processes better explains the speed and accuracy of human information processing.
As a result of these considerations, many contemporary models of knowledge representation emphasize the importance of parallel processing in human cognition.  As a further result of interest in parallel processing, some computers have been made to simulate parallel processing, such as through so-called neural networks of
interlinked computer processors.
At present, many cognitive psychologists are exploring the limits of parallel processing models. According to parallel distributed processing (PDP) models or connectionist models, we handle very large numbers of cognitive operations at once through a network distributed across incalculable numbers of locations in the brain.

How the PDP Model Works
The mental structure within which parallel processing is believed to occur is a network. In connectionist networks, all forms of knowledge are represented within the network structure. Recall that the fundamental element of the network is the node. Each node is connected to many other nodes. These interconnected patterns of nodes enable the individual to organize meaningfully the knowledge contained in the connections among the various nodes. In many network models, each node represents a concept.
The network of the PDP model is different in key respects from the semantic network.  In the PDP model, the network comprises neuron-like units.  They do not, in and of themselves, actually represent concepts, propositions, or
any other type of information. Thus, the pattern of connections represents the knowledge, not the specific units. 


Parallel Distributed Processing Models (PDP)
A class of neurally inspired information processing models that attempt to model information processing the way it actually takes place in the brain.

This model was developed because of findings that a system of neural connections appeared to be distributed in a parallel array in addition to serial pathways. As such, different types of mental processing are considered to be distributed throughout a highly complex neuronetwork.

The PDP model has 3 basic principles: a.) the representation of information is distributed (not local) b.) memory and knowledge for specific things are not stored explicitly, but stored in the connections between units. c.) learning can occur with gradual changes in connection strength by experience.

"These models assume that information processing takes place through interactions of large numbers of simple processing elementscalled units, each sending excitatory and inhibitory signals to other units." (McLelland, J., Rumelhart, D., & Hinton, G., 1986,p.10)

Rumelhart, Hinton, and McClelland (1986) state that there are 8 major components of the PDP model framework:
1.) a set of processing units
2.) a state of activation
3.) an output function for each unit
4.) a pattern of connectivity among units
5.) a propagation rule for propagating patterns of activities through the network of connectivities
6.) an activation rule for combining the inputs impinging on a unit with the current state of that unit to produce a new level of activation for the unit
7.) a learning rule whereby patterns of connectivity are modified by experience

8.) an environment within which the system must operate

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