Receive signals from the hidden units and generate some output based on what is received.

University of Michigan - Ann Arbor

A network following the connectionist approach to cognitive modeling. Contains different kinds of units with connections between them. The units and connections are supposed to resemble neurons and their networks within the brain.

Connectionist network

Goldstein, E. B. (2014). Cognitive psychology: Connecting mind, research and everyday experience. Nelson Education.


Cognitive psychology: Connecting mind, research and everyday experience

Output units (connectionism)

Receives signals from the input units and sends them to the output units.

Hidden units (connectionism)

Activated by stimuli and send signals to the hidden units.

Input units (connectionism)

Determines the extent to which a signal increases or decreases activity of the unit it was sent to.

connection weight

An Artificial Neural Network (ANN) is a computational model inspired by the structure of biological neural networks in the brain, made up of interconnected neuron-like units organized in layers. Rather than following task-specific rules, an ANN learns to recognize patterns in data from examples, adjusting the connections between its units during training.

Artificial Neural Networks

Example of activated connectionist network for living things and their properties

When the network generates wrong responses, an error signal is produced to help the learning process

Error signal

When a part of a connectionist network is damaged, the entire system does not fail, and performance is only affected incrementally. This is similar to the brain, since someone with damage may retain all cognitive functionality aside from the functions that damaged area is specialized to.

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