Decoding sleeping brain activity: Integrating experiments and computational neuroscience to elucidate the cognitive benefit of sleep

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Various brain functions such as perception, learning and memory and decision making emerge from neural computations. Using electrophysiological and computational approaches, we have been specifically focusing on `learning and memory' because it provides the basis of other high-order cognitive functions. Memory is broadly categorized into explicit and implicit memories. Explicit memory is a type of memory that you can recall consciously and verbally describe (for example, the breakfast you had this morning). Implicit memory is a type of memory that you cannot recall consciously nor verbally describe (for example, the motor skill required to ride a bike). Two important questions regarding these memories are how they are represented in the brain and how/when they are consolidated in the neural circuits. A widely supported theory is that a group of anatomically/functionally connected neurons, called a `cell assembly', represents memory. It has been also proposed that consolidation of memory occurs in part during sleep. This memory function of sleep has been supported by experiments showing that behaviourally induced neural activity reactivates during sleep (memory reactivation) and that memory is modified by electrical stimulation during sleep. Although we have these general frameworks for memory and sleep, the term `cell assembly' lacks the stringent definition and we still do not understand how different types of memory are represented by cell assemblies. We do not know how cell assemblies are modified during sleep either. Furthermore, experiments in which memory was modified by electrical stimulation during sleep were challenged by recent negative results. Therefore, in this proposal, we will perform electrophysiological experiments to clarify the mixed experimental results on memory and electrical stimulation, develop a systematic cell assembly detection method to understand how explicit and implicit memories are represented, and develop a computational model to understand how sleep modifies neural circuits and hence memories. The outcomes of this proposal will highlight similarities and differences between explicit and implicit memory, provide better estimates of storage capacity, clarify the effect of electrical stimulation during sleep, and provide better mechanistic insights into how synaptic plasticity during sleep modifies neural activity. The work will also help improve brain-inspired technologies. For instance, deep learning may become more flexible and obtain better categorization ability by implementing memory function of sleep.