A general and small implementation of an artificial neural network in Common Lisp.
A means of investigating learning in a way that is more ‘brain-like’ than traditional, symbolic, approaches, and which has many different possible types, each with different learning rules.
Artificial neural networks are simulations of how it is thought the animal brain operates. It has been found that these simulations possesses powerful pattern recognition and prediction abilities - human-like qualities.
Massively parallel interconnected network of simple adaptive elements, interacting with real world as biological nervous systems do.
a collection of simple artificial neurons connected by directed weighted connections
a model of how the human brain works on the level of individual brain cells
a model of the organic brain
a network of many simple processors ("units"), each possibly having a small amount of local memory
a series of computers which are supposed to learn based on input provided
A model made to simulate a biological neural system; it may model brain processes or brain capabilities.
A processing architecture derived from models of neuron interconnections of the brain. Unlike typical computers, neural networks are supposed to incorporate learning, rather than programming, and parallel, rather than sequential, processing.
Complex dynamical system in which the nodes are analogs of biological neurons, and the connections are arranged so that the whole system performs as an analog of a biological nervous system.
(ANN) A network of neurons that are connected graphically through synapses or weights.
An artificial neural network (ANN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.