Article
Neurosciences
Lea Fritschi, Johanna Hedlund Lindmar, Florian Scheidl, Kerstin Lenk
Summary: According to the tripartite synapse model, astrocytes play a modulatory role in neuronal signal transmission and dysfunction of astrocytes has been linked to psychiatric diseases such as schizophrenia. Various hypotheses have been proposed regarding the pathological mechanisms of astrocytes in schizophrenia, including altered glutamate transmission. However, there is still no consensus on the molecular pathways and network mechanisms altered in schizophrenia. Computational models have shown that impairment of both neurons and astrocytes disrupts neuronal network activity in schizophrenia.
FRONTIERS IN CELLULAR NEUROSCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Thomas L. Athey, Daniel J. Tward, Ulrich Mueller, Laurent Younes, Joshua T. Vogelstein, Michael I. Miller
Summary: The international neuroscience community is developing comprehensive atlases of brain cell types using the theory of jets to improve mapping accuracy. They provide a framework to compute possible errors and demonstrate the effectiveness of their method in both simulated and real neuron traces.
Article
Biology
Rany Abend, Diana Burk, Sonia G. Ruiz, Andrea L. Gold, Julia L. Napoli, Jennifer C. Britton, Kalina J. Michalska, Tomer Shechner, Anderson M. Winkler, Ellen Leibenluft, Daniel S. Pine, Bruno B. Averbeck, Alexander Shackman
Summary: This study used computational models and structural imaging to investigate the links between threat learning, its neuroanatomical substrates, and anxiety severity. The results suggest that anxiety severity is specifically related to slower safety learning and slower extinction of response to safe stimuli.
Article
Cardiac & Cardiovascular Systems
Peter Hanna, Michael J. Dacey, Jaclyn Brennan, Alison Moss, Shaina Robbins, Sirisha Achanta, Natalia P. Biscola, Mohammed A. Swid, Pradeep S. Rajendran, Shumpei Mori, Joseph E. Hadaya, Elizabeth H. Smith, Stanley G. Peirce, Jin Chen, Leif A. Havton, Zixi (Jack) Cheng, Rajanikanth Vadigepalli, James Schwaber, Robert L. Lux, Igor Efimov, John D. Tompkins, Donald B. Hoover, Jeffrey L. Ardell, Kalyanam Shivkumar
Summary: This study provides an in-depth examination of the innervation of the sinoatrial node by the right atrial ganglionated plexus in porcine and human hearts. It demonstrates the significant phenotypic diversity of neurons in the ganglionated plexus and their role in modulating cardiac function. The findings suggest that intrinsic cardiac neurons play a crucial role in controlling specific regions of the heart and could pave the way for targeted therapies in the future.
CIRCULATION RESEARCH
(2021)
Article
Engineering, Mechanical
Quanbao Ji, Xinxin Qie, Min Ye
Summary: This paper presents a computational model that explores integrated information in the bidirectional communication between neurons and astrocytes. The study analyzes the dynamics and information transfer process when stimulating metabotropic glutamate in coupled neuron and astrocyte models. The results demonstrate that the stimulation and coupling strength induce neuronal hyperexcitation, which is partially connected to epileptic instabilities. Additionally, the sustained firing activities of neurons can persist after stimulation cessation due to the time it takes for calcium concentration in astrocytes to reach a steady state. Furthermore, reducing astrocyte feedback effectively inhibits seizure-like firing in neurons from the perspective of neuronal energy consumption.
NONLINEAR DYNAMICS
(2023)
Article
Multidisciplinary Sciences
Pan Zhang, Alicja Omanska, Bradley P. Ander, Michael J. Gandal, Boryana Stamova, Cynthia M. Schumann
Summary: Autism spectrum disorder (ASD) is a heterogeneous disorder with gene and pathway dysregulation in the brain. Transcriptomic analyses were performed on both bulk tissue and laser-capture microdissected neurons from postmortem human brains. Dysregulation of synaptic signaling, inflammation, and RNA splicing were observed in ASD. Alterations in GABA and glutamate signaling pathways were age-dependent. Moreover, dysregulation of inflammatory pathways and splicing disruption were found in ASD neurons.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Mathematics, Interdisciplinary Applications
Tugba Palabas, Joaquin J. Torres, Matjaz Perc, Muhammet Uzuntarla
Summary: An increasing amount of evidence suggests that astrocytes, an abundant type of glial cells in the nervous system, not only support neurons structurally and metabolically, but also modulate neuronal and synaptic functions. However, their role in information processing, especially in the presence of noise, remains unclear. This study investigates the phenomenon of stochastic resonance in neuronal dynamics and shows that astrocytes can enhance the detection of weak signals in the presence of noise, indicating their potential role in noisy neuronal information processing.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Endocrinology & Metabolism
Yuan Ma, Nicola Murgia, Yu Liu, Zixuan Li, Chaweewan Sirakawin, Ruslan Konovalov, Nikolai Kovzel, Yang Xu, Xuejia Kang, Anshul Tiwari, Patrick Malonza Mwangi, Donglei Sun, Holger Erfle, Witold Konopka, Qingxuan Lai, Syeda Sadia Najam, Ilya A. Vinnikov
Summary: This study aimed to identify non-coding genes that regulate metabolic function in mature neurons and found that miR-29a protects against insulin resistance obesity, hyperphagia, decreased energy expenditure, and obesity. The miR-29 family was identified as a key regulator of the PI3K-Akt-mTOR pathway.
MOLECULAR METABOLISM
(2022)
Article
Biochemistry & Molecular Biology
Mathilde Maechler, Joerg Roesner, Iwona Wallach, Joerg R. P. Geiger, Claudia Spies, Agustin Liotta, Nikolaus Berndt
Summary: During general anesthesia, sevoflurane at clinically relevant concentrations affects neuronal activity and decreases energy metabolism while maintaining mitochondrial function.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Multidisciplinary Sciences
Hai-yan He, Arifa Ahsan, Reshmi Bera, Natalie McLain, Regina Faulkner, Kapil V. Ramachandran, Seth S. Margolis, Hollis T. Cline
Summary: Protein degradation by the neuronal membrane proteasome (NMP) is crucial for brain function. It selectively degrades newly synthesized proteins induced by neuronal activity and plays a role in regulating neuronal activity and experience-dependent circuit plasticity.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Neurosciences
Fabien Dumetz, Rachel Ginieis, Corinne Bure, Anais Marie, Serge Alfos, Veronique Pallet, Clementine Bosch-Bouju
Summary: This study investigates the effects of vitamin A deficiency on memory and neuronal plasticity in the hippocampus of rats. The findings suggest that vitamin A deficiency leads to abnormal morphology and synaptic function in hippocampal cells, and moderate-dose vitamin A supplementation can alleviate these abnormalities.
NUTRITIONAL NEUROSCIENCE
(2022)
Article
Biochemistry & Molecular Biology
Yamini Yadav, Chinmoy Sankar Dey
Summary: This study found that PP2C alpha is regulated by insulin through translation under insulin-sensitive and insulin-resistant conditions, which in turn regulates neuronal insulin signaling and insulin resistance.
Review
Biochemistry & Molecular Biology
Ylenia Capodanno, Michael Hirth
Summary: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive solid tumor with a poor prognosis. Chemotherapy can only slightly prolong the survival of unresectable patients. The neural component of the tumor microenvironment, specifically perineural invasion (PNI), has gained attention in pancreatic cancer research. PNI is associated with early tumor recurrence and reduced overall survival. Targeting PNI mechanisms has shown promising results in preclinical models.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Tianfang Zhu, Gang Yao, Dongli Hu, Chuangchuang Xie, Pengcheng Li, Xiaoquan Yang, Hui Gong, Qingming Luo, Anan Li
Summary: This study proposes MorphoGNN, a single neuron morphological embedding based on a graph neural network. By considering the point-level structure information of reconstructed nerve fibers, MorphoGNN captures the lower-dimensional representation of a single neuron and demonstrates cutting-edge performance in tasks such as neuron classification, retrieval, reconstruction quality classification, and neuron clustering.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Neurosciences
Sabine C. Konings, Laura Torres-Garcia, Isak Martinsson, Gunnar K. Gouras
Summary: Synaptic changes and neuronal network dysfunction are early features of Alzheimer's disease. Apolipoprotein E4 (ApoE4) is a major genetic risk factor in AD and can induce hyperexcitability in vulnerable brain regions. ApoE is mainly produced by astrocytes, but neurons can also produce it under stress conditions. This study found that ApoE isoforms from different cellular sources can target synapses and have differential effects on neuronal activity.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Multidisciplinary Sciences
Karl Tuyls, Julien Perolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg
SCIENTIFIC REPORTS
(2018)
Article
Automation & Control Systems
K. N. McGuire, G. C. H. E. de Croon, K. Tuyls
ROBOTICS AND AUTONOMOUS SYSTEMS
(2019)
Proceedings Paper
Automation & Control Systems
Joe Collenette, Katie Atkinson, Daan Bloembergen, Karl Tuyls
DISTRIBUTED AUTONOMOUS ROBOTIC SYSTEMS
(2019)
Proceedings Paper
Automation & Control Systems
James Butterworth, Bastian Broecker, Karl Tuyls, Paolo Paoletti
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18)
(2018)
Proceedings Paper
Automation & Control Systems
Chengwei Zhang, Xiaohong Li, Jianye Hao, Siqi Chen, Karl Tuyls, Zhiyong Feng
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18)
(2018)
Proceedings Paper
Automation & Control Systems
Karl Tuyls, Julien Perolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18)
(2018)
Proceedings Paper
Automation & Control Systems
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18)
(2018)
Proceedings Paper
Automation & Control Systems
Gregory Palmer, Karl Tuyls, Daan Bloembergen, Rahul Savani
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18)
(2018)
Article
Robotics
Richard Klima, Daan Bloembergen, Rahul Savani, Karl Tuyls, Alexander Wittig, Andrei Sapera, Dario Izzo
FRONTIERS IN ROBOTICS AND AI
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Julien Perolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Joscha Fossel, Karl Tuyls, Benjamin Schnieders, Daniel Claes, Daniel Hennes
2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2017)
Article
Robotics
Kimberly McGuire, Guido de Croon, Christophe De Wagter, Karl Tuyls, Hilbert Kappen
IEEE ROBOTICS AND AUTOMATION LETTERS
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017)
(2017)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Joe Collenette, Katie Atkinson, Daan Bloembergen, Karl Tuyls
FOURTEENTH EUROPEAN CONFERENCE ON ARTIFICIAL LIFE (ECAL 2017)
(2017)
Article
Automation & Control Systems
Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska
JOURNAL OF MACHINE LEARNING RESEARCH
(2018)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.