Frequency-based model of memory in neural networks

Autor:	M.B. Moussa, J.C. McKeeman
Rok vydání:	2003
Předmět:	Hopfield network Spiking neural network Probabilistic neural network Recurrent neural network Computer science business.industry Time delay neural network Deep learning Artificial intelligence business Catastrophic interference Algorithm Random neural network
Zdroj:	Proceedings of the IEEE 1988 National Aerospace and Electronics Conference.
DOI:	10.1109/naecon.1988.195171
Popis:	A neural-network model is presented that uses the frequency content of a signal to store and process information. The model is described theoretically and compared to amplitude-based neural networks. A version of the network is also simulated and its storage performance evaluated. A theoretical motivation for such a model is derived from M.L. Minsky's (1986) knowledge-line (k-line) theory of memory. The concept of a k-line is interpreted and associated with a frequency supportive network and a practical model for the implementation of a k-line-based memory is proposed. The implementation of a frequency-based network as a pattern recognizer is considered, along with possible specific applications. >
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::016573aa52afa3dfa3a5d3c542337b45 https://doi.org/10.1109/naecon.1988.195171 Zobrazit plný text záznamu