method-for-selective-rem-sleep-deprivation-using-selective-nose

Method for Selective REM Sleep Deprivation using The Selective Nose Flicking for Forgetting REM (SNiFFer)

Sleep is broadly categorized into Rapid Eye Movement (REM) and non-REM stages, and various sleep deprivation techniques have been developed to study their ...

university of tsukuba

25th Annual Computational Neuroscience Meeting: CNS-2016.

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods.

Multineuron spike train analysis with R-convolution linear combination kernel.

We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitgeber time. To accommodate historical information from each subject, we included a long short-term memory recurrent neural network in combination with the universal features. The resulting system (UTSN-L) achieved 90% overall accuracy and 81% multi-class Matthews Correlation Coefficient, with particularly high-quality judgements for rapid eye movement sleep (91% sensitivity and 98% specificity). This system can enable automatic real-time interventions during rapid eye movement sleep, which has been difficult due to its relatively low abundance and short duration. Further, it eliminates the need for ordinal pre-calibration, electromyogram recording, and manual classification and thus is scalable. The code is open-source with a graphical user interface and closed feedback loop capability, making it easily adaptable to a wide variety of end-user needs. By allowing large-scale, automatic, and real-time sleep stage-specific interventions, this system can aid further investigations of the functions of sleep and the development of new therapeutic strategies for sleep-related disorders.

Real-time, automatic, open-source﻿﻿ sleep stage classification system using single EEG for mice.

Task-nuisance decomposition describes why the information bottleneck loss I(z;x)-βI(z;y) is a suitable objective for supervised learning. The true category  is predicted for input  using latent variables . When  is a nuisance independent from , I(z;n) can be decreased by reducing I(z;x) since the latter upper bounds the former. We extend this framework by demonstrating that conditional mutual information I(z;x|y) provides an alternative upper bound for I(z;n). This bound is applicable even if  is not a sufficient representation of , that is, I(z;y)≠I(x;y). We used mutual information neural estimation (MINE) to estimate I(z;x|y). Experiments demonstrated that I(z;x|y) is smaller than I(z;x) for layers closer to the input, matching the claim that the former is a tighter bound than the latter. Because of this difference, the information plane differs when I(z;x|y) is used instead of I(z;x).

Information Bottleneck Analysis by a Conditional Mutual Information Bound.

Sleep stage-specific intervention is widely used to elucidate the functions of sleep and their underlying mechanisms. For this intervention, it is imperative to accurately classify rapid-eye-movement (REM) sleep. However, the proof of fully automatic real-time REM sleep classification in vivo has not been obtained in mice. Here, we report the in vivo implementation of a system that classifies sleep stages in real-time from a single-channel electroencephalogram (EEG). It enabled REM sleep-specific intervention with 90 % sensitivity and 86 % precision without prior configuration to each mouse. We further derived systems capable of classification with higher frequency sampling and time resolution. This attach-and-go sleep staging system provides a fully automatic accurate and scalable tool for investigating the functions of sleep.

Fully automatic REM sleep stage-specific intervention systems using single EEG in mice.

While memory consolidation is widely believed to require memory reactivation synchronized with theta oscillations during REM sleep, direct causal evidence linking specific neuronal ensembles to this process has been lacking. Strong theta oscillations arise in the hippocampal dentate gyrus, where a small population of principal neurons is continuously generated throughout adulthood. Although these adult-born neurons (ABNs) are known to modulate hippocampal circuits for memory, the causality between their specific information content and memory-related behavior was unknown. Here, we show that REM sleep reactivation of memory ensembles comprising as few as ~3 ABNs is necessary for fear memory consolidation. Crucially, we found that the synchronization of ABN activity with a specific theta phase is essential for associative memory consolidation. Our findings thus provide causal evidence that consolidation critically depends on both the reactivation of minimal neuronal populations and precise neuronal coordination within theta-defined temporal windows.

Article	Year
25th Annual Computational Neuroscience Meeting: CNS-2016. BMC neuroscience\| PubMed ID: 27534393	2016
Multineuron spike train analysis with R-convolution linear combination kernel. Neural networks : the official journal of the International Neural Network Society\| PubMed ID: 29544140	2018
Real-time, automatic, open-source sleep stage classification system using single EEG for mice. Scientific reports\| PubMed ID: 34045518	2021
Information Bottleneck Analysis by a Conditional Mutual Information Bound. Entropy (Basel, Switzerland)\| PubMed ID: 34441114	2021
Fully automatic REM sleep stage-specific intervention systems using single EEG in mice. Neuroscience research\| PubMed ID: 36206953	2023
Transient reactivation of small ensembles of adult-born neurons during REM sleep supports memory consolidation in mice. Nature communications\| PubMed ID: 40764488	2025

Taro Tezuka

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