We suggest that any brain-like (artificial neural network based) learning system will need a sleep-like mechanism for consolidating newly learned information if it wishes to cope with the sequential/ongoing learning of significantly new information. We summarise and explore two possible candidates f
A computational account of dreaming: Learning and memory consolidation
β Scribed by Qi Zhang
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 628 KB
- Volume
- 10
- Category
- Article
- ISSN
- 1389-0417
No coin nor oath required. For personal study only.
β¦ Synopsis
A number of studies have concluded that dreaming is mostly caused by random signals because ''dream contents are random impulses", and argued that dream sleep is unlikely to play an important part in our intellectual capacity. On the other hand, numerous functional studies have suggested that dream sleep does play an important role in our learning and other intellectual functions. Specifically, recent studies have suggested the importance of dream sleep in memory consolidation, following the findings of neural replaying of recent waking patterns in the hippocampus. This study presents a cognitive and computational model of dream process that involves episodic learning and random activation of stored experiences. This model is simulated to perform the functions of learning and memory consolidation, which are two most popular dream functions that have been proposed. The simulations demonstrate that random signals may result in learning and memory consolidation. The characteristics of the model are discussed and found in agreement with many characteristics concluded from various empirical studies.
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