10 papers · ranked by Valyu relevance
Hayato Idei, Keisuke Suzuki, Yuichi Yamashita
Mindfulness has established psychological benefits, such as stress reduction and emotional regulation; however, the underlying computational mechanisms, particularly in relation to mind-wandering, remain unclear. This study aimed to present a hierarchical recurrent neural network-based agent model grounded in the free…
Eliezer Masliah
How transient neural representations become integrated and stable enough to function as internal neural models remains incompletely understood. Grounded in efficient coding, Bayesian and predictive frameworks, recurrent and attractor dynamics, neural state-space models, and systems neuroscience, the Principle of…
Jörn A. Quent, Kaixiang Zhuang, Xinyu Liang, Debin Zeng + 5 more
Detecting novelty is fundamental to adaptive memory, yet the neural comparisons that allow new events to be distinguished from prior experience are not fully understood. Hippocampal novelty signals are often framed as associative comparator responses that register violations of learned relationships. However…
Jalaja Madhusudhanan, Anton Parinov, Charles Fieseler, Manuel Zimmer
Behavior arises from the interplay between spontaneous brain dynamics and sensory-driven responses, yet how spontaneous neural activity shapes variability in decision-making remains unclear. We leverage the tractable C. elegans nervous system to address this question. During oxygen avoidance, we observe binary…
Zichao Jin, Jiaoru Wang, Wenjiang Huang, Jingcheng Zhang + 2 more
Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework.…
Nazanin Zahra Keshvari, Sara Asl Motaleb Nejad Sarkhab, Tara Shahmoradi, Mohammad Pourashory + 3 more
Omics datasets are inherently complex and involve an unmanageable number of factors, which render manual/traditional analyses unfruitful. Through the application of data-training protocols, the large number of parameters could be managed. Moreover, ML methods are generally categorized as either: Supervised ML: the…
Yiming Xue, Xiaojian Liu, Weimin Zhu, Shengfan Wang + 2 more
While protein-RNA interactions are fundamental to post-transcriptional processes, achieving a holistic understanding of their regulatory logic remains challenging. Current computational models often treat binding affinity, interface mapping, and RNA design as isolated tasks, thereby failing to provide a unified…
Maria B. Walter Costa, Rose Brouns, Maria Schreiber, Aristeidis Litos + 5 more
Understanding the adaptations of microorganisms to their environment is key to predicting the stability and dynamics of microbial communities. To uncover molecular mechanisms of environmental response, we extracted genomic features from 13,554 prokaryotic isolates, and trained machine learning models to identify which…
Sahar Alkhaibari, Feng Dong
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early and accurate diagnosis is essential but is often hindered by subjective clinical assessments, limited data availability, and…
Amélie Barozet, Vincent Cabeli, Jean Ogier du Terrail, Alexey Rukhovich + 6 more
The development of climate-resilient crops would be greatly accelerated by models able to reason directly over plant genomic sequences and to pinpoint trait-associated regions or loci. Anticipating the impact of DNA base changes (variants) remains challenging, and understanding regulatory mechanisms is still an active…