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Artificial neural network and Models of neural computation

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Artificial neural network and Models of neural computation

Artificial neural network vs. Models of neural computation

Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof.

Similarities between Artificial neural network and Models of neural computation

Artificial neural network and Models of neural computation have 13 things in common (in Unionpedia): Action potential, Backpropagation, Cognitive architecture, Control theory, Digital data, Genetic algorithm, Gradient descent, Levenberg–Marquardt algorithm, Neural coding, Neuron, Sigmoid function, Spiking neural network, Von Neumann architecture.

Action potential

In physiology, an action potential occurs when the membrane potential of a specific axon location rapidly rises and falls: this depolarisation then causes adjacent locations to similarly depolarise.

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Backpropagation

Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network.

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Cognitive architecture

A cognitive architecture can refer to a theory about the structure of the human mind.

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Control theory

Control theory in control systems engineering deals with the control of continuously operating dynamical systems in engineered processes and machines.

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Digital data

Digital data, in information theory and information systems, is the discrete, discontinuous representation of information or works.

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Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).

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Gradient descent

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.

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Levenberg–Marquardt algorithm

In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems.

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Neural coding

Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble.

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Neuron

A neuron, also known as a neurone (British spelling) and nerve cell, is an electrically excitable cell that receives, processes, and transmits information through electrical and chemical signals.

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Sigmoid function

A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.

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Spiking neural network

Spiking neural networks (SNNs) fall into the third generation of artificial neural network models, increasing the level of realism in a neural simulation.

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Von Neumann architecture

The von Neumann architecture, which is also known as the von Neumann model and Princeton architecture, is a computer architecture based on the 1945 description by the mathematician and physicist John von Neumann and others in the First Draft of a Report on the EDVAC.

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The list above answers the following questions

Artificial neural network and Models of neural computation Comparison

Artificial neural network has 329 relations, while Models of neural computation has 68. As they have in common 13, the Jaccard index is 3.27% = 13 / (329 + 68).

References

This article shows the relationship between Artificial neural network and Models of neural computation. To access each article from which the information was extracted, please visit:

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