Year: 2005
Type: article
Abstract: It is well known that neural systems can suffer catastrophic forgetting of previously learned patterns when trained on new patterns, and that this renders many cognitive models unrealistic.However, through evolution, humans have arrived at mechanisms which minimize this problem, and so in this paper we aim to show how simulated evolution can be used to generate neural network models with significantly less catastrophic forgetting than traditionally formulated models.
Authors: John A. Bullinaria, Tebogo Seipone
Institution: University of Birmingham
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2586379044