: NLP Vocabulary: neuron

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neuron

Elementary component of a layer in an artificial neural network designed for computing the input values to a single output value with an activation function. (Based on ARTES)

Neural networks are organised in layers, including an input and an output layers and, in between, considering one or several hidden layers. Each layer is composed by neurons which is the elementary unit of the network. Each neuron receives one or several inputs for which the neuron performs a weighted sum of the inputs and pass it through a non-linear function (activation) to produce the output. (Costa-Jussà, M. R., From Feature to Paradigm: Deep Learning in Machine Translation, Journal of Artificial Intelligence Research, 2018)

A SOM is an unsupervised machine learning method where artificial neurons are connected to each other by a low dimensional topology (Kohonen 1990). (Burkovski & Heidemann, 2011)
In order to maintain consistency of dimensions the number of neurons is configured as 400 in both the LSTM Layer and the BiLSTM Layer. (Zhou & Wu, 2018)
In the current implementation each variable can be thought of as a bank of artificial neurons. (van Schijndel, Exley & Schuler, 2012)
Most existing neural encoding studies treat each transformer encoding layer in Wav2Vec as a single artificial neuron (AN). (Zhou, Liu, Liu, Wu, Liu, Zhao, Zhu, Guo, Han, Liu & Hu, 2023)
The output layer was a 100-dimensional linear layer with one output neuron. (Pritzen, Gref, Zühlke & Schmidt, 2022)

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