An artificial neural network (ANN), also called a simulated neural network (SNN) (but the term neural network (NN) is grounded in biology and refers to very real, highly complex plexus), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. There is no precise agreed definition among researchers as to what a neural network is, but most would agree that it involves a network of highly complex processing ...

Including:

• Artificial neural network - Background
• Artificial neural network - Models
• Artificial neural network - Learning
• Artificial neural network - Learning paradigms
• Artificial neural network - Learning algorithms
• Artificial neural network - Employing artificial neural networks
• Artificial neural network - Applications
• Artificial neural network - Real life applications
• Artificial neural network - Types of neural networks
• Artificial neural network - Feedforward neural network
• Artificial neural network - Recurrent network
• Artificial neural network - Stochastic neural networks
• Artificial neural network - Modular neural networks
• Artificial neural network - Other types of networks
• Artificial neural network - Theoretical properties
• Artificial neural network - Capacity
• Artificial neural network - Convergence
• Artificial neural network - Generalisation and statistics
• Artificial neural network - Dynamical properties
• Artificial neural network - Related topics
• Artificial neural network - Patents
• Artificial neural network - Bibliography

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See also:

Neural network, Neural network - Characterization, Neural network - The brain neural networks and computers, Neural network - Neural Networks and Artificial Intelligence, Neural network - Background, Neural network - Learning paradigms, Neural network - Learning algorithms, Neural network - Theoretical properties, Neural network - Generalisation and statistics, Neural network - Types of artificial neural networks, Neural network - Neural networks and Neuroscience, Neural network - Types of models, Neural network - Current research, Neural network - References, Neural network - History of the neural network analogy

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In general, a neural network is composed of a group or groups of physically connected or functionally associated neurons. A single neuron can be connected to many other neurons and the total number of neurons and connections in a network can be extremely large. Connections, called synapses are usually formed from axons to dendrites, though dendrodentritic microcircuits [Arbib, p.666] and other connections are possible. Apart from the electrical signalling, there are other forms of signalling that arise from neurotransmitter diffusion, which ...

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Neural network, Neural network - Characterization, Neural network - The brain neural networks and computers, Neural network - Neural Networks and Artificial Intelligence, Neural network - Background, Neural network - Learning paradigms, Neural network - Learning algorithms, Neural network - Theoretical properties, Neural network - Generalisation and statistics, Neural network - Types of artificial neural networks, Neural network - Neural networks and Neuroscience, Neural network - Types of models, Neural network - Current research, Neural network - References, Neural network - History of the neural network analogy

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Connectionism is an approach in the fields of artificial intelligence, cognitive science, neuroscience, psychology and philosophy of mind. Connectionism models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units. There are many different forms of connectionism, but the most common forms utilize neural network models. Connectionism - Basic principles. The central connectionist principle is that mental phenomena can be described by interconnected networks ...

Including:

• Connectionism - Basic principles
• Connectionism - Spreading activation
• Connectionism - Neural networks
• Connectionism - Biological realism
• Connectionism - Learning
• Connectionism - History
• Connectionism - Parallel distributed processing
• Connectionism - Earlier work
• Connectionism - Connectionism apart from PDP
• Connectionism - Connectionism vs. computationalism debate

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In neuroscience, a neural network is a bit of conceptual juggernaut: the conceptual transition from neuroanatomy, a rigorously descriptive discipline of observed structure, to the designation of the parameters delimiting a 'network' can be problematic. In outline a neural network describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signalling targets define a recognizable circuit. Communication between neurons often involves an electrochemical process. The interface through which ...

Including:

• Biological neural network - Early study
• Biological neural network - Connections between neurons
• Biological neural network - Representations in neural networks
• Biological neural network - Philosophical issues
• Biological neural network - Study methods

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Computational neuroscience is an interdisciplinary field which draws on neuroscience, computer science and applied mathematics. It most often uses mathematical and computational techniques such as computer simulations and mathematical models to understand the function of the nervous system. The field of computational neuroscience began with the work of Andrew Huxley, Alan Hodgkin, and David Marr. The results of Hodgkin and Huxley's pioneering work in developing the voltage clamp allowed them to develop the first mathematical mo ...

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Network - Media. Radio network, create and distribute radio programming Television network, create and distribute television programming Network - Electronics Computer Science and data processing. Electrical network, electrical components Digital network, a coupled network of digital components Neural network network (mathematics), a type of graph Network - Computing and telecommunications. < ...

Including:

• Network - Transportation
• Network - Media
• Network - Electronics Computer Science and data processing
• Network - Computing and telecommunications
• Network - Human socialization
• Network - Physics
• Network - Mathematics
• Network - Or

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Cognitive science is usually defined as the scientific study either of mind or of intelligence (e.g. Luger 1994). Practically every introduction to cognitive science also stresses that it is highly interdisciplinary; components of cognitive science include psychology, linguistics, neuroscience, philosophy, computer science, robotics, anthropology and biology. Cognitive science - History. psychology, neuroscience, Neural Darwinism, Society of Mind theory, cognitive science of ...

Including:

• Cognitive science - History
• Cognitive science - Principles of Cognitive Science
• Cognitive science - Approaches
• Cognitive science - Levels of analysis
• Cognitive science - Interdisciplinary nature
• Cognitive science - Cognitive science - The term
• Cognitive science - Scope of cognitive science
• Cognitive science - Artificial intelligence
• Cognitive science - Attention
• Cognitive science - Language processing
• Cognitive science - Learning and development
• Cognitive science - Memory
• Cognitive science - Perception and action
• Cognitive science - Research methods
• Cognitive science - Behavioral experiments
• Cognitive science - Brain imaging
• Cognitive science - Computational modeling
• Cognitive science - Neurobiological methods
• Cognitive science - Key findings
• Cognitive science - Notable researchers in cognitive science and related fields

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David J.C. MacKay (born April 22, 1967) is the professor of natural philosophy in the department of Physics at the University of Cambridge. He was born the fifth child of Donald MacCrimmon MacKay and Valerie MacKay. His contributions in machine learning and information theory include the development of Bayesian methods for neural networks, the rediscovery (with Radford M Neal) of low-density parity-check codes, and the invention of Dasher, soft ...

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Emergence is the process of complex pattern formation from simpler rules. This can be a dynamic process (occurring over time), such as the evolution of the human brain over thousands of successive generations; or emergence can happen over disparate size scales, such as the interactions between a great number of neurons producing a human brain capable of thought (even though the constituent neurons are not individually capable of thought). The original term wa ...

Including:

• Emergence - Emergent properties
• Emergence - Emergence in games
• Emergence - Emergent structures in nature
• Emergence - Emergence in culture and engineering
• Emergence - Emergence in physics
• Emergence - Bibliography

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The ego, superego, and id are the tripartite divisions of the psyche in psychoanalytic theory compartmentalizing the sphere of mental activity into three energetic components: the id being the source of psychological energy derived from instinctual needs and drives. the ego being the organized conscious mediator between the internal person and the external reality. the superego being the internalization of the conscio ...

Including:

• Ego, superego, and id - Freud's structural theory
• Ego, superego, and id - Ego
• Ego, superego, and id - Superego
• Ego, superego, and id - The ego psychologists
• Ego, superego, and id - Defense analysis
• Ego, superego, and id - Criticisms of ego psychology

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Acoustic cryptanalysis is a side channel attack which exploits sounds, audible or not, produced during a computation or input-output operation. In 2004, Dmitri Asonov and Rakesh Agrawal of the IBM Almaden Research Center announced that computer keyboards and keypads used on telephones and automated teller machines (ATMs) are vulnerable to attacks based on differentiating the sound produced by different keys. Their attack employed a neural network to recognize the key being pressed. By analyzing recorded sounds, they were able to recov ...

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Artificial life, also known as alife or a-life, is the study of life through the use of human-made analogs of living systems. Computer scientist Christopher Langton coined the term in the late 1980s when he held the first "International Conference on the Synthesis and Simulation of Living Systems" (otherwise known as Artificial Life I) at the Los Alamos National Laboratory in 1987. Artificial life - Nature of the field. Although the study of artificial life does have some significant overlap w ...

Including:

• Artificial life - Nature of the field
• Artificial life - History and contributions
• Artificial life - Pre-computer
• Artificial life - 1950s-1970s
• Artificial life - 1970s-1980s
• Artificial life - Open problems in AL
• Artificial life - Digital Organism Simulators

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The ego, superego, and id are the tripartite divisions of the psyche in psychoanalytic theory compartmentalizing the sphere of mental activity into three energetic components: the id being the source of psychological energy derived from instinctual needs and drives. the ego being the organized conscious mediator between the internal person and the external reality. the superego being the internalization of the conscio ...

Including:

• Ego superego and id - Freud's structural theory
• Ego superego and id - Id
• Ego superego and id - Ego
• Ego superego and id - Superego
• Ego superego and id - The ego psychologists
• Ego superego and id - Defense analysis
• Ego superego and id - Criticisms of ego psychology

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Main article: Artificial Neural Network Neural network - Background. Neural network models in artificial intelligence are usually referred to as artificial neural networks (ANNs); these essentially simple mathematical models defining a function . The epithet network is used because this function is decomposable into a number of simpler, interconnected elements. A particular type of ANN model corresponds to a class of such functions. What has attract ...

See also:

Neural network, Neural network - Characterization, Neural network - The brain neural networks and computers, Neural network - Neural Networks and Artificial Intelligence, Neural network - Background, Neural network - Learning paradigms, Neural network - Learning algorithms, Neural network - Theoretical properties, Neural network - Generalisation and statistics, Neural network - Types of artificial neural networks, Neural network - Neural networks and Neuroscience, Neural network - Types of models, Neural network - Current research, Neural network - References, Neural network - History of the neural network analogy

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Artificial neural network - Feedforward neural network. The feedforward neural networks are the first and arguably simplest type of artificial neural networks devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network. The earliest kind of neural network is a single-layer perceptron network, which consists of a single layer of output nodes; the inp ...

See also:

Artificial neural network, Artificial neural network - Background, Artificial neural network - Models, Artificial neural network - Learning, Artificial neural network - Learning paradigms, Artificial neural network - Learning algorithms, Artificial neural network - Employing artificial neural networks, Artificial neural network - Applications, Artificial neural network - Real life applications, Artificial neural network - Types of neural networks, Artificial neural network - Feedforward neural network, Artificial neural network - Recurrent network, Artificial neural network - Stochastic neural networks, Artificial neural network - Modular neural networks, Artificial neural network - Other types of networks, Artificial neural network - Theoretical properties, Artificial neural network - Capacity, Artificial neural network - Convergence, Artificial neural network - Generalisation and statistics, Artificial neural network - Dynamical properties, Artificial neural network - Related topics, Artificial neural network - Patents, Artificial neural network - Bibliography

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(main article: Connectionism) The concept of neural networks started in the late-1800s as an effort to describe how the human mind performed. These ideas started being applied to computational models with the Perceptron. In early 1950s Friedrich Hayek was one of the first to posit the idea of spontaneous order in the brain arising out of decentralized networks of simple units (neurons). In the late 1940s, Donnald Hebb made one of the first hypotheses for a mechanism of neural plasticity (i.e. learning), Hebbian learning. ...

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Neural network, Neural network - Characterization, Neural network - The brain neural networks and computers, Neural network - Neural Networks and Artificial Intelligence, Neural network - Background, Neural network - Learning paradigms, Neural network - Learning algorithms, Neural network - Theoretical properties, Neural network - Generalisation and statistics, Neural network - Types of artificial neural networks, Neural network - Neural networks and Neuroscience, Neural network - Types of models, Neural network - Current research, Neural network - References, Neural network - History of the neural network analogy

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While historically the brain has been viewed as a type of computer, and vice-versa, this is true only in the loosest sense. Computers are not models of the brain (even though it is possible to describe a logical process as a computer program, or to simulate a brain using a computer) as they were not created with that purpose in mind. However, neural networks used in artificial intelligence have traditionally been viewed as simplified models of neural processing in the brain. The question of what is the degree of complexity and the pro ...

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Neural network, Neural network - Characterization, Neural network - The brain neural networks and computers, Neural network - Neural Networks and Artificial Intelligence, Neural network - Background, Neural network - Learning paradigms, Neural network - Learning algorithms, Neural network - Theoretical properties, Neural network - Generalisation and statistics, Neural network - Types of artificial neural networks, Neural network - Neural networks and Neuroscience, Neural network - Types of models, Neural network - Current research, Neural network - References, Neural network - History of the neural network analogy

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Perhaps the greatest advantage of ANNs is their ability to be used as an arbitrary function approximation mechanism which 'learns' from observed data. However, using them is not so straightforward and a relatively good understanding of the underlying theory is essential. Choice of model: This will depend on the data representation and the application. Overly complex models tend to lead to problems with learning. Learning algorithm: There are numerous tradeoffs between learning algorithms. Almost any algorithm will work ...

See also:

Artificial neural network, Artificial neural network - Background, Artificial neural network - Models, Artificial neural network - Learning, Artificial neural network - Learning paradigms, Artificial neural network - Learning algorithms, Artificial neural network - Employing artificial neural networks, Artificial neural network - Applications, Artificial neural network - Real life applications, Artificial neural network - Types of neural networks, Artificial neural network - Feedforward neural network, Artificial neural network - Recurrent network, Artificial neural network - Stochastic neural networks, Artificial neural network - Modular neural networks, Artificial neural network - Other types of networks, Artificial neural network - Theoretical properties, Artificial neural network - Capacity, Artificial neural network - Convergence, Artificial neural network - Generalisation and statistics, Artificial neural network - Dynamical properties, Artificial neural network - Related topics, Artificial neural network - Patents, Artificial neural network - Bibliography

Read more here: » Artificial neural network: Encyclopedia II - Artificial neural network - Employing artificial neural networks

Artificial neural network - Feedforward neural network. The feedforward neural networks are the first and arguably simplest type of artificial neural networks devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network. The earliest kind of neural network is a single-layer perceptron network, which consists of a single layer of output nodes; the inp ...

See also:

Artificial neural network, Artificial neural network - Background, Artificial neural network - Models, Artificial neural network - Learning, Artificial neural network - Learning paradigms, Artificial neural network - Learning algorithms, Artificial neural network - Employing artificial neural networks, Artificial neural network - Applications, Artificial neural network - Real life applications, Artificial neural network - Neural network software, Artificial neural network - Types of neural networks, Artificial neural network - Feedforward neural network, Artificial neural network - Recurrent network, Artificial neural network - Stochastic neural networks, Artificial neural network - Modular neural networks, Artificial neural network - Other types of networks, Artificial neural network - Theoretical properties, Artificial neural network - Capacity, Artificial neural network - Convergence, Artificial neural network - Generalisation and statistics, Artificial neural network - Dynamical properties, Artificial neural network - Patents, Artificial neural network - Bibliography

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