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.
Action potential and Artificial neural network · Action potential and Models of neural computation ·
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.
Artificial neural network and Backpropagation · Backpropagation and Models of neural computation ·
Cognitive architecture
A cognitive architecture can refer to a theory about the structure of the human mind.
Artificial neural network and Cognitive architecture · Cognitive architecture and Models of neural computation ·
Control theory
Control theory in control systems engineering deals with the control of continuously operating dynamical systems in engineered processes and machines.
Artificial neural network and Control theory · Control theory and Models of neural computation ·
Digital data
Digital data, in information theory and information systems, is the discrete, discontinuous representation of information or works.
Artificial neural network and Digital data · Digital data and Models of neural computation ·
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).
Artificial neural network and Genetic algorithm · Genetic algorithm and Models of neural computation ·
Gradient descent
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
Artificial neural network and Gradient descent · Gradient descent and Models of neural computation ·
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.
Artificial neural network and Levenberg–Marquardt algorithm · Levenberg–Marquardt algorithm and Models of neural computation ·
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.
Artificial neural network and Neural coding · Models of neural computation and Neural coding ·
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.
Artificial neural network and Neuron · Models of neural computation and Neuron ·
Sigmoid function
A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.
Artificial neural network and Sigmoid function · Models of neural computation and Sigmoid function ·
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.
Artificial neural network and Spiking neural network · Models of neural computation and Spiking neural network ·
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.
Artificial neural network and Von Neumann architecture · Models of neural computation and Von Neumann architecture ·
The list above answers the following questions
- What Artificial neural network and Models of neural computation have in common
- What are the similarities between Artificial neural network and Models of neural computation
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
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