About: Catastrophic interference

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Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. With these networks, human capabilities such as memory and learning can be modeled using computer simulations.

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rdfs:label	Catastrophic interference (en)
rdfs:comment	Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. With these networks, human capabilities such as memory and learning can be modeled using computer simulations. (en)
foaf:depiction
dcterms:subject	Artificial intelligence Machine learning Artificial neural networks
Wikipage page ID	39182554 (xsd:integer)
Wikipage revision ID	1122770121 (xsd:integer)
Link from a Wikipage to another Wikipage	Neural network Artificial intelligence Neocortex Cognitive science Generative model Connectionism Orthogonality Machine learning Feedforward neural network One-hot Backpropagation Machine learning Transfer learning Hippocampus Artificial neural network Artificial neural networks Lookup table Human memory Short term memory Long term memory dbr:Latent_Learning
sameAs	Catastrophic interference Catastrophic interference Catastrophic interference Catastrophic interference
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has abstract	Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. With these networks, human capabilities such as memory and learning can be modeled using computer simulations. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ratcliff (1990). It is a radical manifestation of the 'sensitivity-stability' dilemma or the 'stability-plasticity' dilemma. Specifically, these problems refer to the challenge of making an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionist networks like the standard backpropagation network can generalize to unseen inputs, but they are very sensitive to new information. Backpropagation models can be analogized to human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is an issue when modelling human memory, because unlike these networks, humans typically do not show catastrophic forgetting. (en)
gold:hypernym	Tendency
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foaf:isPrimaryTopicOf	wikipedia-en:Catastrophic_interference
is Link from a Wikipage to another Wikipage of	Online machine learning Varied practice Connectionism Backpropagation Synaptic pruning Artificial neural network Fisher information Catastrophic Forgetting Catastrophic Interference Catastrophic forgetting
is Wikipage redirect of	Catastrophic Forgetting Catastrophic Interference Catastrophic forgetting
is foaf:primaryTopic of	wikipedia-en:Catastrophic_interference

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