
doi: 10.1137/0217071
Neural network memories with storage prescriptions based on Hebb's rule are known to collapse as more words are stored. By requiring that the most recently stored word be remembered precisely, a new simple short- term neural network memory is obtained and its steady state capacity analyzed and simulated. Comparisons are drawn with Hopfield's method, the delta method of Widrow and Hoff, and the revised marginalist model of Mezard, Nadal, and Toulouse.
associative memory, machine learning, neural network, short-term memory, Learning and adaptive systems in artificial intelligence, adaptive systems, Models of computation (Turing machines, etc.)
associative memory, machine learning, neural network, short-term memory, Learning and adaptive systems in artificial intelligence, adaptive systems, Models of computation (Turing machines, etc.)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
