Model mimicking brain's information processing.

Parallel computing architecture for processing.

(machine learning) A neural network in which the outputs are not used as input in a previous layer.

(machine learning) An artificial neural network where the connections between units give them a kind of internal memory useful for processing arbitrary sequences of inputs as in speech recognition.

(machine learning) A neural network in which the connectivity pattern between its neurons is modelled on the organization of the animal visual cortex.

A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs.

Graph neural networks are specialized artificial neural networks that are designed for tasks whose inputs are graphs.

A (sometimes called a twin neural network) an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors.