Data Science: Neuron (Definitions)

[Chaotic neuron:] "An artificial neuron whose output is calculated with the use of a chaotic output function." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

[Oscillatory neuron:] "An artificial neuron built up of two elements (or two groups of elements), one of them being excitatory and the other inhibitory. Its functioning is described as oscillation, characterized by three parameters: frequency; phase; amplitude." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"A nerve cell in the physiological nervous system." (Guido J Deboeck and Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

[Hidden neuron:] "Usually a nonlinear (or linear) processing element with no direct connections to either inputs or outputs. It often provides the learning capacity of the neural network." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps", 2000)

"any of the numerous types of specialized cell in the brain or other nervous systems that transmit and process neural signals. The nodes of artificial neural networks are also called neurons." (Teuvo Kohonen, "Self-Organizing Maps 3rd Ed.", 2001)

"A single processing element in a neural network. The most common form of neuron has two basic parts: a summation function that receives inputs and a transfer function that processes inputs and passes the processed values to the next layer of neurons. If the neuron is in the last layer of the network, the output is the final estimate of the dependent variable for that input vector or case." (David Scarborough & Mark J Somers, "Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior", 2006)

"the elementary processing unit that composes an ANN." (Pablo Escandell-Montero et al, "Artificial Neural Networks in Physical Therapy", 2015)

"A neuron takes a number of input values (or activations) as input and maps these values to a single output activation. This mapping is typically implemented by applying a multi-input linear-regression function to the inputs and then pushing the result of this regression function through a nonlinear activation function, such as the logistic or tanh function." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"Specialized brain cell that integrates inputs from other neurons and sends outputs to other neurons." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"A unit in a neural net whose function (such as y = tanh(w.dot(x))) takes multiple inputs and outputs a single scalar value. This value is usually the weights for that neuron (w or wi) multiplied by all the input signals (x or xi) and summed with a bias weight (w0) before applying an activation function like tanh. A neuron always outputs a scalar value, which is sent to the inputs of any additional hidden or output neurons in the network. If a neuron implements a much more complicated activation function than that, like the enhancements that were made to recurrent neurons to create an LSTM, it is usually called a unit, for example, an LSTM unit." (Hobson Lane et al, "Natural Language Processing in Action: Understanding, analyzing, and generating text with Python", 2019)

"An artificial neuron is a model of a neuron present in an animal brain that is perceived as a mathematical function." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)