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A widely used type of composition is the nonlinear weighted sum, where () = (()), where (commonly referred to as the activation function [3]) is some predefined function, such as the hyperbolic tangent, sigmoid function, softmax function, or rectifier function. The important characteristic of the activation function is that it provides a smooth ...
An artificial neuron may be referred to as a semi-linear unit, Nv neuron, binary neuron, linear threshold function, or McCulloch–Pitts (MCP) neuron, depending on the structure used. Simple artificial neurons, such as the McCulloch–Pitts model, are sometimes described as "caricature models", since they are intended to reflect one or more ...
Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. In machine learning , a neural network (also artificial neural network or neural net , abbreviated ANN or NN ) is a model inspired by the structure and function of biological neural ...
An axo-axonic synapse is a type of synapse, formed by one neuron projecting its axon terminals onto another neuron's axon. [1]Axo-axonic synapses have been found and described more recently than the other more familiar types of synapses, such as axo-dendritic synapses and axo-somatic synapses.
It was named after Richard FitzHugh (1922–2007) [2] who suggested the system in 1961 [3] and Jinichi Nagumo et al. who created the equivalent circuit the following year. [4]In the original papers of FitzHugh, this model was called Bonhoeffer–Van der Pol oscillator (named after Karl-Friedrich Bonhoeffer and Balthasar van der Pol) because it contains the Van der Pol oscillator as a special ...
The preliminary theoretical base for contemporary neural networks was independently proposed by Alexander Bain [4] (1873) and William James [5] (1890). In their work, both thoughts and body activity resulted from interactions among neurons within the brain. Computer simulation of the branching architecture of the dendrites of pyramidal neurons [6]
In a parallel after-discharge circuit, a neuron inputs to several chains of neurons. Each chain is made up of a different number of neurons but their signals converge onto one output neuron. Each synapse in the circuit acts to delay the signal by about 0.5 msec, so that the more synapses there are, the longer is the delay to the output neuron.
The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear .