Connectionism and Network Models
Network models of memory storage emphasize the role of connections between stored memories in the brain. The basis of these theories is that neural networks connect and interact to store memories by modifying the strength of the connections between neural units. In network theory, each connection is characterized by a weight value that indicates the strength of that particular connection. The stronger the connection, the easier a memory is to retrieve.
Network models are based on the concept of connectionism. Connectionism is an approach in cognitive science that models mental or behavioral phenomena as the emergent processes of interconnected networks that consist of simple units. Connectionism was introduced in the 1940s by Donald Hebb, who said the famous phrase, "Cells that fire together wire together." This is the key to understanding network models: neural units that are activated together strengthen the connections between themselves.
There are several types of network models in memory research. Some define the fundamental network unit as a piece of information. Others define the unit as a neuron. However, network models generally agree that memory is stored in neural networks and is strengthened or weakened based on the connections between neurons. Network models are not the only models of memory storage, but they do have a great deal of power when it comes to explaining how learning and memory work in the brain, so they are extremely important to understand.
Parallel Distributed Processing Model
The parallel distributed processing (PDP) model is an example of a network model of memory, and it is the prevailing connectionist approach today. PDP posits that memory is made up of neural networks that interact to store information. It is more of a metaphor than an actual biological theory, but it is very useful for understanding how neurons fire and wire with each other.
Taking its metaphors from the field of computer science, this model stresses the parallel nature of neural processing. "Parallel processing" is a computing term; unlike serial processing (performing one operation at a time), parallel processing allows hundreds of operations to be completed at once—in parallel. Under PDP, neural networks are thought to work in parallel to change neural connections to store memories. This theory also states that memory is stored by modifying the strength of connections between neural units. Neurons that fire together frequently (which occurs when a particular behavior or mental process is engaged many times) have stronger connections between them. If these neurons stop interacting, the memory's strength weakens. This model emphasizes learning and other cognitive phenomena in the creation and storage of memory.
Neural connections
As neurons form connections with each other through their many dendrites, they can form complex networks. Network models propose that these connections are the basis of storing and retrieving memories.