Sleep and Wakefulness Research

Últimos artículos

• In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models

  Benjamin Smith, Levin Kuhlmann, Kaushik Roy, Gideon Kowadlo · 21 de agosto de 2026

  Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this area compared to biological learning systems. Machine learning algorithms typically trade off retention of previously learned information and adaptatio…

• StageGuard: Physiologically Constrained Sleep Staging

  Juntang Wang, Yihan Wang, Hao Wu, Jiayu Gao, Shixin Xu, Dongmian Zou · 28 de julio de 2026

  Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficiency, and bout-duration statistics. Deep learning models achieve epoch-level accuracy approaching inter-rater agreement, yet often produce hypnograms…

• Not Just After One: Sleep-Inspired Replay Prevents Catastrophic Forgetting After Sequential Tasks

  Anthony Bazhenov, Jean Erik Delanois, Giri P. Krishnan · 9 de junio de 2026

  One of the critical limitations of artificial neural networks is their lack of ability to continually learn: training on new tasks often leads to interference and forgetting of the previous ones. While several algorithms have been proposed to protect old memories from interference, they are typicall…

• From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysis

  Niklas Grieger, Siamak Mehrkanoon, Philipp Ritter, Stephan Bialonski · 26 de mayo de 2026

  Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection,…

• SCM: Sleep-Consolidated Memory with Algorithmic Forgetting for Large Language Models

  Saish Sachin Shinde · 24 de abril de 2026

  We present SCM (Sleep-Consolidated Memory), a research preview of a memory architecture for large language models that draws on neuroscientific principles to address a fundamental limitation in current systems: the absence of persistent, structured, and biologically plausible memory. Existing approa…

• SleepNet and DreamNet: Enriching and Reconstructing Representations for Consolidated Visual Classification

  Mingze Ni, Wei Liu · 9 de abril de 2026

  An effective integration of rich feature representations with robust classification mechanisms remains a key challenge in visual understanding tasks. This study introduces two novel deep learning models, SleepNet and DreamNet, which are designed to improve representation utilization through feature …

• A large corpus of lucid and non-lucid dream reports

  Remington Mallett · 31 de marzo de 2026

  All varieties of dreaming remain a mystery. Lucid dreams in particular, or those characterized by awareness of the dream, are notoriously difficult to study. Their scarce prevalence and resistance to deliberate induction make it difficult to obtain a sizeable corpus of lucid dream reports. The conse…

• Learning to Forget: Sleep-Inspired Memory Consolidation for Resolving Proactive Interference in Large Language Models

  Ying Xie · 17 de marzo de 2026

  Large language models (LLMs) suffer from proactive interference (PI): outdated information in the context window disrupts retrieval of current values. This interference degrades retrieval accuracy log-linearly as stale associations accumulate, a bottleneck that persists regardless of context length …

• Slumbering to Precision: Enhancing Artificial Neural Network Calibration Through Sleep-like Processes

  Jean Erik Delanois, Aditya Ahuja, Giri P. Krishnan, Maxim Bazhenov · 10 de marzo de 2026

  Artificial neural networks are often overconfident, undermining trust because their predicted probabilities do not match actual accuracy. Inspired by biological sleep and the role of spontaneous replay in memory and learning, we introduce Sleep Replay Consolidation (SRC), a novel calibration approac…

• A systematic approach to answering the easy problems of consciousness based on an executable cognitive system

  Qi Zhang · 6 de marzo de 2026

  Consciousness is the window of the brain and reflects many fundamental cognitive properties involving both computational and cognitive mechanisms. A collection of these properties was described as the "easy problems" by Chalmers, including the ability to discriminate, categorize, and react to stimul…

• A computational account of dreaming: learning and memory consolidation

  Qi Zhang · 5 de febrero de 2026

  A number of studies have concluded that dreaming is mostly caused by randomly arriving internal 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 contrary, numerous functional studies have reve…