Parallel Distributed Processing
Parallel Distributed Processing (PDP) models are 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:
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. (Rumelhart, Hinton, & McClelland, 1986, p. 10)
- the representation of information is distributed (not local)
- memory and knowledge for specific things are not stored explicitly, but stored in the connections between units.
- learning can occur with gradual changes in connection strength by experience.
Rumelhart, Hinton, and McClelland (1986) state that there are 8 major components of the PDP model framework:
- a set of processing units
- a state of activation
- an output function for each unit
- a pattern of connectivity among units
- a propagation rule for propagating patterns of activities through the network of connectivities
- 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
- a learning rule whereby patterns of connectivity are modified by experience
- an environment within which the system must operate
- Rumelhart, D.E., Hinton, G.E., & McClelland, J.L. (1986). A general framework for parallel distributed processing. In D. E. Rumelhart, J. L. McClelland, and the PDP Research Group (Eds.). Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations. Cambridge, MA: MIT Press.