Continuous learning, sleep and memory consolidation

Friday, September 28, 2018 -
2:00pm to 3:00pm
The FUNG Auditorium
Maksim Bazhenov


Department of Medicine

University of California, San Diego

Continuous learning, sleep and memory consolidation


Memory depends on three general processes: encoding, consolidation and retrieval. Although the vast majority of research has been devoted to understanding encoding and retrieval, recent novel approaches have been developed in both human and animal research to probe mechanisms of consolidation. A story is emerging in which important functions of consolidation occur during sleep and that specific features of sleep appear critical for successful retrieval across a range of memory domains, tasks, and species.

Previously encoded memories can be damaged by encoding of new memories, especially when they are relevant to the new data and hence can be disrupted by new training – a phenomenon called “catastrophic forgetting”. Sleep can prevent the damage by replaying recent memories along with the old relevant memories. Though multiple evidences point to the role of sleep in memory consolidation, exact mechanisms remain to be understood. In our study, using computer models based on empirical data, we explored the neural substrates of memory consolidation involving replay of memory traces during slow-wave sleep.

Our study predicts that spontaneous reactivation of the learned neuronal sequences during sleep spindles and slow waves of NREM sleep represents a key mechanism of memory consolidation and the basic structure of sleep stages provides an optimal environment for consolidation of competing memories.



Dr. Bazhenov received his Ph.D. in physics and mathematics from Nizhny Novgorod State University in 1994. He did postdoctoral training at The Institute of Applied Physics of The Russian Academy of Sciences and later at The Salk Institute for Biological Studies. Dr. Bazhenov joined Department of Cell Biology and Neuroscience at the University of California, Riverside as Associate Professor in 2008. He is currently Professor of Medicine at the University of California, San Diego. The ultimate goal of Dr. Bazhenov's research is to understand how the brain processes and learns, the underlying mechanisms behind brain activities in normal and pathological states. To address these questions, he uses a broad spectrum of approaches including computational modeling and in vitro electrophysiology. Dr. Bazhenov's specific research interests include: Sleep and memory consolidation, Reinforcement learning and decision making, Information coding by neuronal networks, Neuronal mechanisms of epileptic seizures.

By providing funds to organize focused conferences and workshops

Improving memory consolidation by stimulation during sleep

Recording (electrophysiology, imaging) from large enough ensembles of neurons during sleep

Understanding neuronal mechanisms of memory consolidation during REM sleep

What can we learning from memory consolidation in humans and animals that can be used in AI systems