(also called artificial neural network, abbr. ANN). A network where the nodes correspond to neurons and the arcs correspond to synaptic connections in the biological metaphor. The following properties are included:
- neural state. Each node has a state variable, say x. In the brain, this could be the potassium level; in computing applications, it could be anything the modeler chooses.
- arc weight. Each arc has a weight that affects the state of its neighboring nodes when firing. If the weight is positive, it said to be excitatory; if it is negative, it is inhibitory.
- state equations. The neural states change by some differential (or difference) equation, say Typically (but not necessarily), is the gradient of an energy function (in keeping with the biological metaphor), say so that follows a path of steepest descent towards a minimum energy state.
- learning mechanism. This could be equations to change the weights: Various learning mechanisms are represented this way, including a form of supervised learning that uses a training set to provide feedback on errors. Other elements can be learned besides the arc weights, including the topology of the network.