A data processing architecture ('type of computer') based on analogy of the connectionism of the human brain. Many types, many training algorithms. Useful primarily for pattern recognition tasks in the presence of imprecise or noisy data. Big application in signal processing.

Not a network of devices, but an artificially intelligent processing method within a computer that allows self learning from experience.

A mathematical model of the nervous system found in humans and animals. Applications in such areas as character recognition and shape recognition have produced noteworthy results.

A representation of a human brain similar in that both a brain and a neural network consist of input neurons gathering information from an external environment, synapses which interlace the input neurons' information in complex but fairly predictable patterns, and output neurons which turn the patterns of the synapses into actions made on the external environment. Both a brain and neural network are capable of evolution over multiple organisms' lifetimes and learning within a single organism's lifetime.

A type of statistical computer program which classifies large and complex data sets by grouping cases together in a way similar to the human brain. Used in data mining.

A field of artificial intelligence in which millions of chips (processing elements) are interconnected to enable computers to imitate the way the human brain works.

level: Comprehensive (3) [ order by level] In human memory processing, a complex of associated groups of circuits organized and linked by common meanings.

a form of artificial intelligence software that imitates some functions of the human brain, such as a learning capacity.

A neural network is a computer network designed to function in a similar way to natural neural structures such as a human brain.

Personal computer modeling software that helps determine the weights to be applied to a large number of variables to predict purchase response of a target audience.

computer architecture in which processors are connected in a manner suggestive of connections between neurons; can learn by trial and error

any network of neurons or nuclei that function together to perform some function in the body

a biological model of a human brain, simulated in the binary memory of your PC

a circuit composed of a very large number of simple processing elements that are neurally based

a circuit designed to replicate the way neurons act and interact in the brain

a collection of layers of neurons, simulating the human brain structure

a complex computer model that is capable of learning

a computational model that is loosely based on the neuron cell structure of the biological nervous system

a computation model which uses many relatively simple interconnected units ( nodes ) working in parallel

a computer architecture modeled on the human brain, consisting of nodes connected to each other by links of differing strengths

a computer architecture modelled upon the human brain's interconnected system of neurons which mimics its information processing, memory and learning processes

a computer system that can learn which combinations of inputs (representing team performance statistics, say) lead to a particular output (such as a team's chance of winning)

a computer system that mimics the activities of the brain

a computing system, inspired by the human brain, that learns to perform functions rather than having functions programmed into it

a computing system made up of a number of simple, highly interconnected processing elements, which processes information by its dynamic state response to external elements

a crude type of artificial intelligence in which multiple connections link to the processing units ( nodes or artificial neurons ) in parallel

a function that maps a set of input values to a set of output values

a mathematical method for identifying discriminating

a mathematical model that performs prediction or classification using numeric data

a mathematical or computational model for information processing based on a connectionist approach to computation

a mathematical representation of the human brain

a mathematical system loosely modeled on the human brain

a modeling technique based on the observed behavior of biological neurons and used to mimic the performance of a system (e

an analysis method that uses a large number of relatively simple calculations to make a prediction

an artificial-intelligence processing method within a computer that allows self-learning from experience

an artificial Intelligence program that uses a network of connections between input values and output values to eventually learn how to do something

a Network modeled after neurons in brain tissues and is a set of neurons in an input layer connected to one or more hidden layers of neurons which are in turn connected to an output layer of neurons

an example of a nonlinearregression model

an example of a nonlinear regression model

an example of 'bottom-up' approach when there is an attempt to study the biological mechanisms that underlie human intelligence and to build machines, which work on similar principles

an information-processing network, which is inspired by the manner in which a human brain performs a particular task or function of interest

an information processing structure which is inspired by the way the mammalian brain processes information

an information - processing system consisting of a collection of simple processing elements or "nodes

an input-output mapping that accepts input patterns (i

an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron

an interconnected group of neuron s

an interconnected groups of nodes, akin to the vast network of neuron s in the human brain

a numerical model of many

a paradigm for modelling the behaviour of the human brain

a parallel-distributed information processing structure consisting of processing elements (neurons) interconnected via unidirectional signal channels called connections

a pattern recognition device that assimilates data and learns to recognize patterns

a pattern recognition system that simulates in software the way neurons in the human brain recognise the information in parallel patterns

a popular mathematical model with a number of variants for producing an output, discrete or continuous, from multiple inputs that often share no linear relationship

a powerful data modeling tool that is able to capture and represent complex input/output relationships

a powerful technique for a computer program to make decisions, recognising things that it has never seen before by their closeness to previously seen patterns

a processing device, either an algorithm , or actual hardware, whose design was inspired by the design and functioning of animal brains and components thereof

a program that simulates the way a biological brain works - or at least the way some people think it might work

a rough mathematical model of a human brain represented by a directed graph where the vertices are called neurons and the arcs connections

a series of computers which are supposed to learn based on input provided

a set of inter-connected neurons

a set of processing units (the neurons) defined by an Input Function, a Transfer Function and an Output Function

a software implementation or model of the way scientists believe individual neurons in the brain behave

a statistical construct adept at inferring functions and thereby mapping inputs to corresponding outputs

a style of program that specializes in pattern recognition, using interconnected software pieces (organized not unlike neurons in your brain, hence the name) to process information much more rapidly than a conventional sequential program would do

a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes

a system for modeling processes and time series data, such as price data

a system of programs and data structures approximating the operation of the human brain

a type of information processing system whose architecture is similar to the structure of biological neural systems

a type of model that can be used to predict continuously-valued outputs or to classify observations

a way of allowing a system to learn based on previous experience

An interconnected system of brain cells. Neurophysiology: The physiological study of the nervous system.

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.

A simplified emulation of the connections of the human brain, used for investigating learning and self-organisation within an artificial environment.

Software and hardware simulation of the human brain using artificial neurons combined in a massively parallel network.

A computational device made of units that resemble neurons. Such networks are often used to simulate brain activity.

A real or virtual device modeled after the human brain that involves the connection between several elements that work together to learn.

A class of computational methods that loosely imitate the function of the brain. Among the benefits of neural networks are that they learn from experience, can generalize from their data set, are fault tolerant, and can exploit parallel systems for rapid processing.

A computer architecture in which processors are interconnected in a fashion similar to the connections of the neurons in a human brain; this is in contrast with the prevalent traditional linear logical architecture. A neural net is able to learn by a process of trial and error and is especially big in the development of artificial intelligence.

Highly parallel dynamic system that carries out information processing by means of its overall state response to continuous or initial input. A mathematical model of the brain's neurons. Sometimes thought of as feedback amplifiers connected in parallel. Also referred to as artificial neural system, natural intelligence, and neurocomputer.

order by term] level: Comprehensive (3) In human memory processing, a complex of associated groups of circuits organized and linked by common meanings.

Computer programs loosely modelled after the functioning of the human nervous system. A type of artificial intelligence pioneered by Bernard Widrow of Stanford University that recognises and records sequences and patterns and makes subsequent evaluations based on previous conclusions. Such conclusions are approximations, and not definitive.

A form of artificial intelligence in which a computer simulates the way a human brain processes information.

Computer program or system designed to mimic some aspects of neurone connections, including summation of action potentials, refractory periods and firing thresholds.

an artificial brain constructed out of self-growing crystals. Extremely fast but not very intelligent, neural networks form the basic computer of the Gears.

See Connectionist Model.

(n.) Man made devices using interconnects and processing capabilities suggested by models of the cortex are termed neural networks. These systems are widely used for optimization problems including content addressable memories and pattern recognition.

Interconnected group of neurons.

Usually used to mean "artificial neural network," a computer program inspired by simple models of the brain. It consists of a network of nodes connected by weighted links that establish the relationships between nodes. Each node sums the weighted inputs entering it and compares the result to a (usually) nonlinear function to produce its own output. Most neural networks have a training rule establishing how the weights are adjusted to bring the average output closest to that desired. The term can also mean the computer chip containing the fully trained (unmodifiable) system used in automatic sensors and controls. See also machine learning.

Computer programs loosely modeled after the biological structure of the human brain; they consist of thousands of interconnected computing units called neurons. An artificial neural network can be constructed to simulate the action of a series of neurons, the analogous word being "processing unit." A neural net is normally constructed by arranging processing units in a number of layers. In its simplest form, a neural net consists of three layers: an input layer, an output layer, and a hidden layer (de la Garza and Rouhana, 1995).

A processing architecture derived from models of neuron interconnections of the brain. Typically different from computers by incorporating learning rather than programming and parallel rather than sequential processing.

Computer system that attempts to simulate the behavior of the human brain by connecting thousands of processors together much like the neurons in the brain are connected. 11.22

A system modeled after the neurons (nerve cells) in a biological nervous system. A neural network is designed as an interconnected system of processing elements, each with a limited number of inputs and outputs. Rather than being programmed, these systems learn to recognize patterns.

A software system loosely based on how the brain works. It tries to simulate the multiple layers of elements called neurons. Each neuron is tied to several neighbors with a value that signifies the strength of the connections. Learning is accomplished by changing the values to cause the network to report appropriate results. Neural networks have been used for market forecasts and other applications.

A complex nonlinear modeling technique based on a model of a human neuron. A neural net is used to predict outputs (dependent variables) from a set of inputs (independent variables) by taking linear combinations of the inputs and then making nonlinear transformations of the linear combinations using an activation function. It can be shown theoretically that such combinations and transformations can approximate virtually any type of response function. Thus, neural nets use large numbers of parameters to approximate any model. Neural nets are often applied to predict future outcome based on prior experience. For example, a neural net application could be used to predict who will respond to a direct mailing.

Artificial Neural Networks (ANN) are non-linear predictive models that learn through training. They atempt to emulate the processing of a biological brain.

A neural network, also known as a parallel distributed processing network, is a computing solution that is loosely modeled after cortical structures of the brain. It consists of interconnected processing elements called nodes or neurons that work together to produce an output function. The output of a neural network relies on the cooperation of the individual neurons within the network to operate.

a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experiential knowledge and making available for use

a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use

a set of independent processors that are connected to all adjacent processors