A team of US researchers has for the first time decoded how deep sleep -- also called slow-wave sleep -- is involved in promoting the consolidation of recent memories in our brain.
Earlier research strongly suggested that sleep, which constitutes about a third of our lives, is crucial for learning and forming long-term memories. But how such memory is formed has not been well understood.
The new study by researchers at University of California-Riverside tried to answer this question using a computational model.
During sleep, human and animal brains are primarily decoupled from sensory input.
The brain, however, remains highly active, showing electrical activity in the form of sharp-wave ripples in the hippocampus part of the brain and large-amplitude slow oscillations in the cortex region.
Traces of episodic memory acquired during wakefulness and initially stored in the hippocampus are progressively transferred to the cortex as long-term memory during sleep.
The team's model, presented recently in the Journal of Neuroscience, showed that patterns of slow oscillations in the cortex are influenced by the hippocampal sharp-wave ripples and that these patterns of slow oscillations determine synaptic changes in the cortex.
The model also showed that the synaptic changes, in turn, affect the patterns of slow oscillations, promoting a kind of reinforcement and replay of specific firing sequences of the cortical neurons - representing a replay of specific memory.
"Input from the hippocampus - the sharp-wave ripples - determines the spatial and temporal pattern of these slow oscillations," said study lead author Yina Wei.
"By influencing the nature of these oscillations, this hippocampal input activates selective memories during deep sleep and causes a replay of specific memories. During such memory replay, the corresponding synapses are strengthened for long-term storage in the cortex," Wei added.
These results suggest the importance of the hippocampal sharp-wave ripple events in transferring memory information to the cortex, she noted.
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