Humans spend the lion's share of their mental life either in their personal past or an anticipated or imagined future. This type of mental state is known as mental time travel. It is perhaps the most sophisticated and fitness-promoting cognition that has evolved in humans and with some reservation in animals. We have proposed that working memory capacity and the complexity of executive functions within working memory might limit the authenticity with which past events are reconstructed and anticipated or imagined future scenarios are constructed. In the present article, we discuss the possibility of a co-evolution between working memory capacity, complexity of executive functions available in the working memory workspace, and mental time travel abilities across species. We further assume that a complex working memory system can be constructed with quite different brains and conclude that the advanced cognitive function of thinking about the past and the future might not be a privilege of the mammalian brain.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.neubiorev.2019.07.016 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Pattern memory cannot be completely and truly realized in deep neural networks.
Sci Rep
December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.
View Article and Find Full Text PDFCerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation.
Nat Commun
December 2024
Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.
The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.
View Article and Find Full Text PDFTrivalent recombinant protein vaccine induces cross-neutralization against XBB lineage and JN.1 subvariants: preclinical and phase 1 clinical trials.
Nat Commun
December 2024
Laboratory of Aging Research and Cancer Drug Target, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
The immune escape capacities of XBB variants necessitate the authorization of vaccines with these antigens. In this study, we produce three recombinant trimeric proteins from the RBD sequences of Delta, BA.5, and XBB.
View Article and Find Full Text PDFRNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.
Brief Bioinform
November 2024
In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan.
Accurate prediction of RNA modifications holds profound implications for elucidating RNA function and mechanism, with potential applications in drug development. Here, the RNA-ModX presents a highly precise predictive model designed to forecast post-transcriptional RNA modifications, complemented by a user-friendly web application tailored for seamless utilization by future researchers. To achieve exceptional accuracy, the RNA-ModX systematically explored a range of machine learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Transformer-based architectures.
View Article and Find Full Text PDFDeep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.
Adv Sci (Weinh)
December 2024
Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong SAR, China.
The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi-class classification.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!