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In the context of [[artificial neural network]], '''pruning''' is the practice of removing [[parameter]]s (which may entail removing individual parameters, or parameters in groups such as by [[artificial neurons|neurons]]) from an existing network.<ref>{{cite arXiv|last1=Blalock|first1=Davis|last2=Ortiz|first2=Jose Javier Gonzalez|last3=Frankle|first3=Jonathan|last4=Guttag|first4=John|date=2020-03-06|title=What is the State of Neural Network Pruning?|class=cs.LG|eprint=2003.03033}}</ref> The goal of this process is to maintain accuracy of the network while increasing its [[efficiency]]. This can be done to reduce the [[computational resource]]s required to run the neural network. A biological process of [[synaptic pruning]] takes place in the brain of mammals during development<ref>{{Cite journal |last1=Chechik |first1=Gal |last2=Meilijson |first2=Isaac |last3=Ruppin |first3=Eytan |date=October 1998 |title=Synaptic Pruning in Development: A Computational Account |url=https://ieeexplore.ieee.org/document/6790725 |journal=Neural Computation |volume=10 |issue=7 |pages=1759–1777 |doi=10.1162/089976698300017124 |pmid=9744896 |s2cid=14629275 |issn=0899-7667}}</ref> (see also [[Neural Darwinism]]).
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#Remove the least important neuron.
#Check a termination condition (to be determined by the user) to see whether to continue pruning.
== Edge (weight) pruning ==
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