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Classification of teak wood production in Central Java using the C5.0 algorithm

AIP Conference Proceedings 2019年第1期2202卷

作者： Yuliana Susanti Respatiwulan Sri Sulistijowati Handajani Hasih Pratiwi Isnandar Slamet Hartatik Firstiana Istiqomah 1Program Study of Statistics Universitas Sebelas Maret Indonesia 2Diploma Program of Informatics Engineering Universitas Sebelas Maret Surakarta Indonesia

Central Java has the largest teak land area in Java Island. It is about two-thirds of teak land area on Java Island is located in Central Java. The purpose of this study is to determine the classification of factors that influence the production of teak trees in Central Java with the Commercial Version 5.0 (C5.0) algorithm. This algorithm is a refinement of the ID3 and C4.5 algorithms. The C5.0 algorithm works by separating samples based on attributes that have the highest information gain. The results of the study show that there are three classification rules produced with an 81.8% accuracy rate.

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Dynamic ¹³C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin (vol 23, 100855, 2020)

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ISCIENCE 2020年第12期23卷 100855页

作者： Quek, Lake-Ee Krycer, James R. Ohno, Satoshi Yugi, Katsuyuki Fazakerley, Daniel J. Scalzo, Richard Elkington, Sarah D. Dai, Ziwei Hirayama, Akiyoshi Ikeda, Satsuki Shoji, Futaba Suzuki, Kumi Locasale, Jason W. Soga, Tomoyoshi James, David E. Kuroda, Shinya Charles Perkins Centre The University of Sydney Sydney NSW 2006 Australia School of Mathematics and Statistics The University of Sydney Sydney NSW 2006 Australia Sydney Medical School The University of Sydney Sydney NSW 2006 Australia Department of Biological Sciences Graduate School of Science University of Tokyo Hongo 7-3-1 Bunkyo-ku Tokyo 113-0033 Japan School of Life and Environmental Sciences The University of Sydney Sydney NSW 2006 Australia Institute for Advanced Biosciences Keio University Tsuruoka Yamagata 997-0052 Japan YCI Laboratory for Trans-Omics Young Chief Investigator Program RIKEN Center for Integrative Medical Sciences 1-7-22 Suehiro-cho Tsurumi-ku Yokohama Kanagawa 230-0045 Japan Faculty of Engineering and Information Technologies The University of Sydney Sydney NSW 2006 Australia Department of Pharmacology and Cancer Biology Duke University School of Medicine Duke University Durham NC 27710 USA AMED-CREST AMED 1-7-1 Otemachi Chiyoda-Ku Tokyo 100-0004 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8562 Japan CREST Japan Science and Technology Agency Bunkyo-ku Tokyo 113-0033 Japan

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13 C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

关键词： Biological Sciences Flux Data Metabolomics Metabolic Flux Analysis

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Steady Rayleigh-BénardRayleigh-Bénard convection between stress-free boundaries

arXiv

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arXiv 2020年

作者： Wen, Baole Goluskin, David LeDuc, Matthew Chini, Gregory P. Doering, Charles R. Department of Mathematics University of Michigan Ann ArborMI48109-1043 United States Department of Mathematics & Statistics University of Victoria VictoriaBCV8P 5C2 Canada Department of Physics University of Michigan Ann ArborMI48109-1040 United States Program in Integrated Applied Mathematics University of New Hampshire DurhamNH03824 United States Department of Mechanical Engineering University of New Hampshire DurhamNH03824 United States Center for the Study of Complex Systems University of Michigan Ann ArborMI48109-1042 United States

Steady two-dimensional Rayleigh-Bénard convection between stress-free isothermal boundaries is studied via numerical computations. We explore properties of steady convective rolls with aspect ratios π/5 ⩽ Γ ⩽ 4π, where Γ is the width-to-height ratio for a pair of counter-rotating rolls, over eight orders of magnitude in the Rayleigh number, 103 ⩽ Ra ⩽ 1011, and four orders of magnitude in the Prandtl number, 10−2 ⩽ Pr ⩽ 102. At large Ra where steady rolls are dynamically unstable, the computed rolls display Ra → ∞ asymptotic scaling. In this regime, the Nusselt number Nu that measures heat transport scales as Ra1/3 uniformly in Pr. The prefactor of this scaling depends on Γ and is largest at Γ ≈ 1.9. The Reynolds number Re for large-Ra rolls scales as Pr−1Ra2/3 with a prefactor that is largest at Γ ≈ 4.5. All of these large-Ra features agree quantitatively with the semi-analytical asymptotic solutions constructed by Chini & Cox (2009). Convergence of Nu and Re to their asymptotic scalings occurs more slowly when Pr is larger and when Γ is smaller. Copyright © 2020, The Authors. All rights reserved.

关键词： Prandtl number

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Metabolic profile of biodiverse diets in a healthy European cohort

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The American Journal of Clinical Nutrition 2025年

作者： Bernadette Chimera Emine Koc Cakmak Jessica Blanco-Lopez Jeroen Berden Carine Biessy Pekka Keski-Rahkonen Geneviève Nicolas Justine Berlivet Carl Lachat Bernard Srour Kris A Murray Paolo Vineis Mathilde Touvier Oliver JK Robinson Giles Hanley-Cook Lorenzo Mangone Raul Zamora-Ros Rosario Tumino Jytte Halkjær Agnetha Rostgaard-Hansen Inge Huybrechts International Agency for Research on Cancer (IARC/WHO) Nutrition and Metabolism Branch 25 avenue Tony Garnier LYON CEDEX 07 France Medical Research Council Centre for Environment and Health School of Public Health Imperial College London UK Department of Food Technology Safety and Health Faculty of Bioscience Engineering Ghent University Ghent Belgium Université Sorbonne Paris Nord and Université Paris Cité INSERM INRAE CNAM Center for Research in Epidemiology and Statistics (CRESS) Nutritional Epidemiology Research Team (EREN) Bobigny France Centre on Climate Change and Planetary Health MRC Unit The Gambia at London School of Hygiene and Tropical Medicine Atlantic Boulevard Fajara The Gambia Unit of Nutrition and Cancer Cancer Epidemiology Research Program Catalan Institute of Oncology (ICO) Bellvitge Biomedical Research Institute (IDIBELL) 08908 Barcelona Spain Hyblean Association for Epidemiology Research AIRE ONLUS Ragusa Italy Danish Cancer Institute at the Danish Cancer Society Strandboulevarden 49 Copenhagen Denmark

Background There is increasing evidence that diets characterized by food biodiversity could contribute to health outcomes. Greater dietary species diversity has been linked to reduced gastrointestinal cancer risk and all-cause mortality. However, mechanistic pathways supporting the association between food biodiversity and health are just beginning to be explored. Aim To characterize the metabolic profile associated with food biodiversity of diets in a pan-European population. Methods Dietary species richness (DSR), or the absolute number of unique species in an individual’s diet, was calculated for 7,983 cancer-free control participants within the European Prospective Investigation into Cancer and Nutrition cohort study. Usual dietary intakes in the preceding year were assessed at recruitment with country-specific dietary questionnaires. Metabolomic profiles from blood which included 128 circulating endogenous metabolites,32 polyphenol compounds, and 39 fatty acid isomers were used as biomarkers of potential mechanisms underlying nutrition and health associations. Lasso regression identified key metabolites in discovery and replication sets, and multivariable stepwise linear regression were used to quantify associations between DSR and metabolomic profiles. Results A total of 52 metabolites were selected using Lasso regression in the replication set, of which 70% were statistically significant in stepwise linear regression. Higher DSR was associated with lower levels of 4 amino acids (e.g., tyrosine, -0.0231, 95% CI: -0.0362, -0.0100, p = 0.0009) and 2 acylcarnitines (e.g., C14:2, -0.0834, 95% CI: -0.1084, -0.0583, p < 0.0001). Conversely, higher levels were observed for 7 amino acids (e.g., tryptophan, -0.0954, 95% CI: -0.1049, -0.0860, p < 0.0001) and 9 polyphenols (e.g., epicatechin, p = 0.0017). Conclusion In this European middle-aged adult population, the circulating metabolic profiles of biodiverse diets are consistent with the metabolome linked with health-p

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Sensory Profile of Fermented Arabica Coffee in the Perception of American Cupping Tasters

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Agricultural Sciences 2019年第3期10卷 321-329页

作者： Lucas Louzada Pereira Rogério Carvalho Guarconi Tais Rizzo Moreira Dério Brioschi Jr. Joao Paulo Pereira Marcate Luiz Henrique Bozzi Pimenta de Sousa Aldemar Polonini Moreli Danieli Grancieri Debona Carla Schwengber Ten Caten Federal Institute of Education Science and Technology of Espírito Santo Venda Nova do Imigrante CampusFood Science and Technology ProgramRua Elizabeth Minete PerimVitóriaBrazil Capixaba Institute for Technical Assistance Research and ExtensionINCAPERDepartment of StatisticsDomingos MartinsBrazil Department of Forest Sciences and Wood Federal University of Espirito SantoCenter for Agrarian Sciences and EngineeringJeronimo MonteiroBrazil Department of Production and Transportation Engineering Federal University of Rio Grande do SulPorto AlegreBrazil

The consumption of specialty coffees has been growing year after year, indicating that several niches and market forms according to the sensorial orientation of the consumers, as well as the cupping tasters and agents are responsible for the composition of blends, and for the physical and sensory classification of the coffees in the industry. This study presented 5 methods of spontaneous and induced fermentation to different North American cupping tasters, in order to evaluate coffee quality potential, based on the Specialty Coffee Association (SCA) protocol. During the months of May to July of 2017, 150 coffee samples were evaluated. Initial results indicate that North American cupping tasters evaluated dry fermented coffees using yeast culture as superiors, in the sensorial aspect, than the more conventional methods in Brazil. Such indication opens new discussions on the acceptance of a determined product, followed by the potential for modification of the sensory route as of the fermentation that will affect the attributes.

关键词： Arabica Coffee Fermentation Acceptance Cupping Tasters

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Spatiotemporal development of expanding bacterial colonies driven by emergent mechanical constraints and nutrient gradients

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Nature communications 2025年第1期16卷 4878页

作者： Harish Kannan Hui Sun Mya Warren Tolga Çağlar Pantong Yao Brian R Taylor Kinshuk Sahu Daotong Ge Matteo Mori David Kleinfeld JiaJia Dong Bo Li Terence Hwa Department of Mathematics University of California San Diego La Jolla CA USA. Department of Mathematics and Statistics California State University Long Beach CA USA. paul.sun@csulb.edu. Department of Physics University of California San Diego La Jolla CA USA. San Diego Supercomputer Center University of California San Diego La Jolla CA USA. Neurosciences Graduate Program University of California San Diego La Jolla CA USA. Joint BioEnergy Institute Emeryville CA USA and Systems Biology Department Sandia National Laboratories Livermore CA USA. Department of Bioengineering University of California San Diego La Jolla CA USA. Department of Biomedical Engineering Duke University Durham NC USA. Department of Neurobiology University of California San Diego La Jolla CA USA. Department of Physics & Astronomy Bucknell University Lewisburg PA USA. jiajia.dong@bucknell.edu. Department of Mathematics and Ph.D. Program in Quantitative Biology University of California San Diego La Jolla CA USA. bli@ucsd.edu. Department of Physics University of California San Diego La Jolla CA USA. hwa@ucsd.edu.

Bacterial colonies growing on solid surfaces can exhibit robust expansion kinetics, with constant radial growth and saturating vertical expansion, suggesting a common developmental program. Here, we study this process for Escherichia coli cells using a combination of modeling and experiments. We show that linear radial colony expansion is set by the verticalization of interior cells due to mechanical constraints rather than radial nutrient gradients as commonly assumed. In contrast, vertical expansion slows down from an initial linear regime even while radial expansion continues linearly. This vertical slowdown is due to limitation of cell growth caused by vertical nutrient gradients, exacerbated by concurrent oxygen depletion. Starvation in the colony interior results in a distinct death zone which sets in as vertical expansion slows down, with the death zone increasing in size along with the expanding colony. Thus, our study reveals complex heterogeneity within simple monoclonal bacterial colonies, especially along the vertical dimension. The intricate dynamics of such emergent behavior can be understood quantitatively from an interplay of mechanical constraints and nutrient gradients arising from obligatory metabolic processes.

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A Stochastic MPEC Approach for Grid Tariff Design with Demand Side Flexibility

TechRxiv

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TechRxiv 2019年

作者： Askeland, Magnus Burandt, Thorsten Gabriel, Steven A. SINTEF Energy Research Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Technical University of Berlin The Energy Transportation Environment Department German Institute for Economic Research DIW Berlin Berlin Germany Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim Norway Department of Mechanical Engineering Applied Math & Statistics Scientific Computation Program University of Maryland College Park Maryland United States

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs are one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results for a deterministic example and a stochastic case study are presented and discussed. © 2019, CC BY-SA.

关键词： Decision making

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Influence of autapses on synchronisation in neural networks with chemical synapses

arXiv

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arXiv 2020年

作者： Protachevicz, Paulo R. Iarosz, K.C. Caldas, I.L. Antonopoulos, C.G. Batista, A.M. Kurths, J. Institute of Physics University of São Paulo São Paulo SP Brazil Faculdade de Telêmaco Borba FATEB Telêmaco Borba Paraná Brazil Graduate Program in Chemical Engineering Federal University of Technology Paraná Ponta GrossaParaná Brazil Department of Mathematical Sciences University of Essex Wivenhoe Park United Kingdom Department of Mathematics and Statistics State University of Ponta Grossa Ponta Grossa PR Brazil Department Complexity Science Potsdam Institute for Climate Impact Research Potsdam Germany Department of Physics Humboldt University Berlin Germany Centre for Analysis of Complex Systems Sechenov First Moscow State Medical University Moscow Russia

A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronisation. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behaviour. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behaviour depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses. Copyright © 2020, The Authors. All rights reserved.

关键词： Synchronization

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