# Neural network

### From Glossary

(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.