- Comp-neuro - lists.cnsorg.org

ESI SyNC 2023: September 14 - 15

by El Mahmoudi, Nada

[https://www.esi-frankfurt.de/img/esisync/esisync2023_header.jpg] Linking hypotheses: where neuroscience, computation, and cognition meet Dear Neuroscientists, We are happy to announce that the Ernst Strüngmann Institute Systems Neuroscience Conference (ESI SyNC) 2023 will take place in-person and online this year, on September 14 and 15 at the Ernst Strüngmann Institute in Frankfurt, Germany. This year’s topic is: Linking hypotheses: where neuroscience, computation, and cognition meet. Eleven speakers will discuss their research and views on the intersection of systems, behavioral, and computational neuroscience from a wide array of viewpoints: from basic network interactions in the brain probed at the electrophysiological and mechanistic levels, to complex animal behavior and cognition. This year’s conference is an outstanding platform bringing together sub-fields in the neurosciences that find themselves, regrettably often, treated in isolation. Young researchers will have the opportunity to exchange ideas and raise questions in formal and informal discussions. There will also be a poster session in person for which we welcome submissions. This conference is free for online participants and researchers in Frankfurt am Main. Registration Link: https://survey.academiccloud.de/index.php/763913?lang=en List of speakers: https://www.esi-frankfurt.de/newevent/ Best Regards, ESI-Sync Organizing Committee

Aug. 4, 2023

Postdoc position in computational systems neuroscience, Voigts Lab, Janelia

by Voigts, Jakob

The Voigts Lab at HHMI Janelia Research Campus is recruiting postdocs to work on how neocortex learns and applies models of the world to enable complex cognitive abilities. We will study flexible cognition by examining naturalistic behaviors that require animals to perform sequential computations, yet are learned quickly. We'll be able to throw whatever technology is needed at the problem of combining sufficient measurements with complex naturalistic behavior in mice, e.g. large-scale electrophysiology or calcium imaging, unrestrained behavior in large complex environments, closed-loop manipulations, circuit reconstruction, etc., but more importantly we'll work on the challenge of modeling, analyzing, and ultimately learning from data when there are no strictly repeated trials to average over. The postdoc position could focus on experimental and/or computational approaches to these questions, depending on the interests and experience of the candidate. We're part of the new Mechanistic Cognitive Neuroscience research area at Janelia. Postdocs will have access to an unusual amount of resources and support, allowing us to make rapid progress on these challenging questions. We'll also work closely with experimental, theoretical, methods, and machine learning researchers in- and outside of Janelia to make this research program possible, collaborative, and fun. Our lab website is https://www.voigtslab.org<https://www.voigtslab.org/>. To apply, please send a brief statement of research interests and CV to voigtsj(a)janelia.hhmi.org<mailto:voigtsj@janelia.hhmi.org>

Aug. 3, 2023

Two postdoc positions in neuroAI

by Rui Ponte Costa

Dear all, We are seeking to appoint two Postdoctoral Research Scientists to join the Neural and Machine Learning Group part of the world-class Oxford Neuroscience and the Oxford Neurotheory initiative. You will carry out research at the intersection between computational neuroscience and machine learning, closely informed by experimental observations. More details here: shorturl.at/hkwG3<https://t.co/d7ugbOLFDV> and of our group: neuralml.github.io<https://neuralml.github.io/> Best wishes, Rui Ponte Costa Group Leader, Neural and Machine Learning group<https://neuralml.github.io/> Centre for Neural Circuits and Behaviour | Oxford Neurotheory Department of Physiology, Anatomy and Genetics Department of Experimental Psychology (honorary member) University of Oxford, UK

Aug. 2, 2023

[Job][PhD] at Inria Bordeaux, FR: From Reservoir Transformers to BrainGPT

by Xavier Hinaut

PhD position available at Inria, Bordeaux, France: "From Reservoir Transformers to BrainGPT" # Keywords Transformers; Reservoir Computing; Computational Neuroscience # Duration & start date 3 years starting 1st October 2023 (estimated) # Contact & Application Informal contact/questions and application: email to xavier dot hinaut at inria dot fr Application & More info: https://recrutement.inria.fr/public/classic/en/offres/2023-06611 All positions available: https://github.com/neuronalX/phd-and-postdoc-positions Deadline: Applications will be considered on a rolling basis. A candidate will be selected as soon as a suitable profile is found. # Context This PhD thesis is part of the BrainGPT "Inria Exploratory Action" project. In the wake of the emergence of large-scale language models such as ChatGPT, the BrainGPT project is at the forefront of research in Artificial Intelligence and Computational Neuroscience. These models, although remarkably powerful, do not reflect the way in which our brains process and learn language. BrainGPT is rising to the challenge by focusing on the development of models that are more faithful to human cognitive functioning, inspired by data on brain activity during listening or reading. The ambition is to create more efficient models that are less dependent on intensive calculations and massive volumes of data. BrainGPT will open up new perspectives on our understanding of language and cognition. # Project and Work description The rapid rise in performance of language models, as demonstrated by the recent appeal of ChatGPT, is undeniable. However, the computational cost and environmental impact associated with such models are often overlooked [1]. These models rely on Transformers [2], which facilitate unsupervised learning on a large volume of data. These same models are used to predict brain activity from functional magnetic resonance imaging (fMRI) or magnetoencephalography (MEG), an application our team also exploits [3]. The main ambition of the BrainGPT project is to combine the explainability of mechanistic models with the predictive power of Transformers to analyze brain imaging data. Today, on the one hand we have explanatory but less predictive mechanistic models, such as those based on Reservoir Computing, and on the other hand, high-performance predictive models, but not explanatory, like Transformers. Our goal is to combine the best of these two approaches, to develop more efficient ("sample efficient") models inspired by Transformers, which more faithfully reflect the how the brain works, while improving the predictive power of mechanistic models. Towards this ambition, the BrainGPT project seeks to identify the key mechanisms that allow Transformers to predict brain activity. Furthermore, our project strives to build models that are more biologically plausible than Transformers, incorporating the most relevant components for predicting brain activity, while integrating constraints derived from human cognition studies. The long-term objectives of the BrainGPT project are as follows: - Making Transformers more biologically plausible, which could improve the prediction of brain activity by imaging (fMRI, MEG, etc.). - Proposing new perspectives and computing paradigms that do not rely exclusively on gradient backpropagation, given its high computational and energy cost. - Reducing the energy footprint of Transformers by minimizing the computational costs associated with their learning. In summary, the thesis will mainly consist of developing new bio-inspired models inspired by the mechanisms, learning methods, and emerging behaviors of Large Language Models (LLMs) and Transformers. Subsequently, in collaboration with our collaborators, these models will be tested to assess their ability to predict brain activity from imaging data. [1] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proc. of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623). [2] Vaswani, A. et al. (2017) Attention is all you need. In Proc. of Advances in neural information processing systems. [3] Oota, S. R., Trouvain, N., Alexandre, F., & Hinaut, X. (2023, August). MEG Encoding using Word Context Semantics in Listening Stories. In Proc. of INTERSPEECH 2023. # Skills Ideal Candidate Profile: - Holds an engineering or scientific degree and/or a PhD in digital sciences (computer science, automation, signal processing). - Has a first professional experience (6 months of internship or more) in Machine Learning and Python development. Especially in one or more of the following: Recurrent Neural Networks (in particular Reservoir Computing), Transformers, Large Language Models. - Possesses strong expertise in the scientific Python software and scientific stack (numpy/scipy). - Demonstrates a solid grasp of linear algebra concepts. - Proficiency in technical English is crucial, as it enables efficient collaboration with our international partners and effective presentations at conferences. - Has proven experience with version management, familiarity with Git, and proficiency in using the GitHub platform. # Advisor & Location Xavier Hinaut Inria Bordeaux & Institute for Neurodegenerative diseases (Pellegrin Hospital Campus, Bordeaux). Best regards, Xavier Hinaut Inria Research Scientist www.xavierhinaut.com -- +33 5 33 51 48 01 Mnemosyne team, Inria, Bordeaux, France -- https://team.inria.fr/mnemosyne & LaBRI, Bordeaux University -- https://www4.labri.fr/en/formal-methods-and-models & IMN (Neurodegeneratives Diseases Institute) -- http://www.imn-bordeaux.org/en --- Our Reservoir Computing library: https://github.com/reservoirpy/reservoirpy

Aug. 2, 2023

[JOBS] Tenure Track Assistant Professor Intelligent Systems @ Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, the Netherlands.

by Kerstin Bunte

Tenure Track Assistant Professor Intelligent Systems https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… Closing date = 17-9-2023 We offer a Tenure Track Assistant Professor position with a research profile in Intelligent Systems within the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, The Netherlands. Intelligent Systems aim to give computers and cyber-physical systems the abilities to perceive, analyze and learn from data, make decisions, and enhance human capabilities. The research area has experienced tremendous growth in recent years, especially due to the increase in incorporation of intelligent systems into nearly every aspect of our lives. The societal relevance of sustainability, efficiency, robustness, understanding and guarantees of algorithms is gaining more and more importance, not only in software but also in combination with cyber-physical systems. Relevant aspects are: the theory of machine learning, artificial intelligence and the mathematics of computing (ACM Computing Classification System, 2012 Revision). For this position, possible research directions include (but are not limited to) the theory and application of novel machine learning and artificial intelligence methods with an emphasis on solving pressing open questions, such as efficiency, transparency and interpretability, fairness, the integration of multi-modal and complex heterogeneous data, causal inference, continuous machine learning, generative and dynamic systems. Complex problems demand an increasing amount of interdisciplinary and cross-domain knowledge. Therefore, backgrounds in information theory, probabilistic representations, causality and computational geometry are also welcome to apply. As Assistant Professor with a focus on research, you will: - set up and develop your own research line and research group - supervise PhD students - acquire external funding - promote the societal relevance of your research - teach in and contribute to the development of the degree programs of the School of Science and Engineering, especially courses with computing science content - contribute to the organization of the faculty, for example by participating in working groups and committees, in the domains of teaching, research and management. The position will be embedded in the Intelligent Systems research unit. Our research areas include biologically inspired computational modeling, the theory of machine learning and algorithmic development, as well as image processing with mathematical morphology, machine learning and AI. National and international research collaborations span a multitude of application domains, including medical sciences and health care, natural sciences (astronomy, bioinformatics), remote sensing, computer vision, robotics, smart industry and engineering. About the University of Groningen Since its foundation in 1614, the University of Groningen has enjoyed an international reputation as a dynamic and innovative center of higher education offering high-quality teaching and research. Balanced study and career paths in a wide variety of disciplines encourage currently more than 37,000 students and researchers to develop their own individual talents. Belonging to the best research universities in Europe and the top 100 universities in the world, the University of Groningen is truly an international place of knowledge. More information can be found at: https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… Kind regards, Kerstin -- Kerstin Bunte, Associate Professor | Rosalind Franklin Fellow Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence Faculty of Science and Engineering University of Groningen P.O. Box 407, 9700 AK Groningen The Netherlands Email: k.bunte(a)rug.nl web: http://www.cs.rug.nl/~kbunte/

Aug. 2, 2023

Advanced Electrophysiology Post-doc Position Available - Interventional Psychiatry Program, Minneapolis, MN, USA

by Hiba Sheheitli

*About the Job* Dr. Ziad Nahas (Interventional Psychiatry Lab) in the University of Minnesota Department of Psychiatry and Behavioral Sciences is seeking an outstanding candidate for a postdoctoral position to conduct and analyze the effects of neuromodulation on brain activity in mood disorders. Candidates should be passionate about advancing knowledge in the area of translational research of depressive disorders and other mental health conditions with a focus on invasive and non-invasive brain stimulation treatments. The position is available June 1, 2023, and funding is available for at least two years. *Qualifications* Required Qualifications: ●PhD in relevant field (neuroscience, psychology, cognitive science) ●Strong programming and/or data analysis skills. MATLAB or Python is essential. C/C++ (and derivatives), LabVIEW, and scripting languages could be useful. Prior experience with data acquisition hardware including behavioral as well as EEG would be helpful. Proficiency with Microsoft Office software ●Experience with neurophysiology (human or animal), psychophysics and/or brain stimulation is ideal. ●Excellent verbal and written communication skills, with specific documentation of those skills through published work and oral scientific presentations. ●Demonstrated ability to work independently and to independently overcome complex technical challenges. ●Interpersonal skills appropriate for collaboration in a very multidisciplinary and highly diverse environment. Preferred Qualifications: •Previous experience with psychiatric patient populations, the biological mechanisms of mental illness, and/or medical device development. •Signal processing and/or computer science background. •Previous experience with human functional imaging or electrophysiology. *About the Department* Dr. Ziad Nahas’s Lab is located primarily at the Interventional Psychiatry Program’s St. Louis Park clinic, with additional study activity at UMN and M Health Fairview sites on the UMN campus. Our work focuses on advancing translational research of mood dysregulation and depressive disorders. Specific interests and areas of expertise include: ● Improving patient care of treatment resistant disorders using neuromodulation techniques. ● Functional neuroimaging (fMRI), electroencephalography (EEG) and brain stimulation modalities: Transcranial magnetic stimulation (TMS), transcranial alternating current stimulation (tACS), vagus nerve stimulation (VNS), prefrontal cortical stimulation (PCS), deep brain stimulation (DBS), electroconvulsive therapy (ECT) and focal electrically administered seizure therapy (FEAST). ● Increasing equitable access to neuromodulation interventions for patient care. To learn more about Dr Ziad Nahas and the Department of Psychiatry & Behavioral Sciences, please visit: https://med.umn.edu/bio/psychiatry/ziad-nahas *Apply here: * https://hr.umn.edu/Jobs/Find-Job <https://hr.umn.edu/Jobs/Find-Job> (Click on External Candidates and search for 355003 to find the Job Posting)

Aug. 1, 2023

NeurIPS Workshop "Information-Theoretic Principles in Cognitive Systems"

by Rava A. da Silveira

NeurIPS Workshop: "Information-Theoretic Principles in Cognitive Systems" CALL FOR PAPERS Website (with submission information): https://sites.google.com/view/infocog-neurips-2023/call-for-papers Contact: infocog.neurips2023(a)gmail.com Important Dates: * Paper submission deadline: September 20, 2023 * Author notification: October 27, 2023 * Camera ready deadline: November 27, 2023 The second InfoCog@NeurIPS workshop aims to further the understanding of information flow in cognitive processes and neural networks models of cognition by bringing together researchers from multiple disciplines, including machine learning, cognitive science, neuroscience, and information theory. This is the second workshop of this kind in NeurIPS. This year the workshop will continue last year’s community-building momentum and will additionally focus on advancing two main goals: First, providing a fruitful platform for discussing formulations of storage and processing of information in human and artificial cognitive systems, via information-theoretic measures. Specifically, we aim to further integrate information theory researchers in such discussions, deepening the mathematical knowledge and tools shared across disciplines. [NZ1] Second, facilitating research on addressing the challenges of computing information theoretic quantities, specifically for the needs of cognitive science and machine learning. We invite short submissions (up to 4 pages – NeurIPS format) that present original work related to information theory and cognitive systems, and particularly encourage submissions of collaborative interdisciplinary work. Examples of specific topics of interest include but are not limited to: * Novel information-theoretic approaches to cognitive functions (e.g., perception, decision making, language, social reasoning, etc.) * Method and approaches for the validation of information theoretic formalisms in human and artificial cognition * Novel methods and approaches for the computation and/or estimation of information theoretic quantities and their applications to human and artificial cognition * Challenges and limitations of the use of information theory in studying cognitive systems * Application of information theory to training human-aligned artificial agents, i.e., agents that can better communicate and cooperate with humans All accepted papers will be presented in a poster session during the workshop, and a number of papers will also be selected for an oral presentation. We expect to have limited resources to offer financial aid to selected participants. More details will follow upon acceptance.

July 30, 2023

Postdoc in computational modeling at Stanford Cognitive & Systems Neuroscience Lab

by Mai-Phuong K Bo

The Stanford Cognitive and Systems Neuroscience Laboratory (scsnl.stanford.edu) invites applications for a postdoctoral fellowship in human brain network analysis and modeling. We seek candidates with strong research backgrounds in one or more of the following areas: resting-state and task-evoked fMRI connectivity, structural HARDI-based connectivity, neurophysiological modeling of brain networks, Bayesian modeling, machine learning, and deep neural networks. Candidates must have strong computational skills and a PhD in computational neuroscience, computer science, physics, biomedical engineering or related fields. This is an exciting opportunity to work on multiple NIH- and Stanford-funded projects using multimodal (fMRI, optogenetic, DTI, EEG) brain imaging techniques in multiple clinical populations and normative groups. The successful candidate will develop a vigorous research program that contributes to, and complements, ongoing research studies. The candidate will have access to state-of-the-art brain imaging and HPC/GPU facilities.  Please email a CV, statement of research interests and relevant background, and have three letters of reference sent to Prof. Vinod Menon at  scsnl.stanford+postdoc(a)gmail.com<mailto:scsnl.stanford@gmail.com> Mai-Phuong Bo (She/Her) Lab & Recruitment Manager Stanford Cognitive & Systems Neuroscience Lab Department of Psychiatry & Behavioral Sciences Email: maipbo(a)stanford.edu [cid:93b74359-266e-493c-9def-d9fb37f515df]

July 27, 2023

PhD position at the TU Delft at the crossroads of machine learning and hardware design

by justin＠dauwels.com

Dear colleagues, We are looking for a highly motivated Ph.D. candidate with a background in machine learning and/or in electrical engineering for a project at the TU Delft at the crossroads of machine learning and hardware design. Building autonomous agents that can reliably compute and take decisions in noisy and uncontrolled environments is among the top research areas in today's artificial intelligence (AI). Yet, doing so within constrained power budgets for battery-operated edge devices is currently an open challenge. Indeed, while current cutting-edge deep-learning approaches can now reach acceptable performance in such environments, they are still subject to adversarial attacks and need a backend of GPU clusters, thereby requiring 4 to 6 orders of magnitude more power than is allowed for by the edge power budgets. On both ends of the spectrum of learning algorithms are the error backpropagation algorithm, i.e. the workhorse of modern deep learning, and local Hebbian learning rules, which are inspired by the brain's synaptic plasticity mechanisms. The former offers excellent performance but its energy/memory footprint is incompatible with low-power edge devices, while the latter allows for low-cost hardware implementations but can hardly be deployed beyond toy problems. In this PhD project, you will tackle this challenge by: * developing a theoretical framework that will allow exploring and generating, in a use-case-driven fashion, emerging learning algorithms that combine the strengths of local brain-inspired learning and backprop, * with a hardware/algorithm co-design approach, developing custom neuromorphic silicon prototypes (digital, then mixed-signal) for the proposed learning rules * investigating the deployment of these adaptive prototypes in resource-constrained use cases at the edge, such as brain implants for seizure detection. This project is a collaboration between Dr. Charlotte Frenkel (neuromorphic hardware, hardware/algorithm co-design, brain-inspired machine learning) and Dr. Justin Dauwels (Bayesian machine learning, computational neuroscience, biosignal processing). About the Department of Microelectronics at TU Delft: https://microelectronics.tudelft.nl/ About the CogSys research lab: https://ei.et.tudelft.nl/Research/theme.php?id=63 The expected starting date is 01/11/2023 and deadline for application is 01/09/2023. For more information and to apply, please visit this website <https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details? jobId=13043&jobTitle=PhD%20Position%20in%20Bio-plausible%20Local%20Learning% 20Rules%20for%20Adaptive%20Neuromorphic%20Hardware> . Greetings, Justin. _______________________________________________________________ Justin Dauwels j.h.g.dauwels(a)tudelft.nl <mailto:j.h.g.dauwels@tudelft.nl> Associate Professor http://cas.tudelft.nl/ Fac. EEMCS Section Circuits and Systems Mekelweg 4 2628 CD Delft, The Netherlands _____________________________________________________________________

July 27, 2023

SUBMIT YOUR WORK TO COMPLEX NETWORKS 2023 BEFORE SEPT. 02, 2023

by Hocine Cherifi

*12* *th **International Conference on Complex Networks & Their Applications* Menton Riviera <https://www.menton-riviera-merveilles.co.uk/>, France November 28 - 30, 2023 COMPLEX NETWORKS 2023 <https://complexnetworks.org/> You are cordially invited to submit your contribution until *September 02, 2023*. *SPEAKERS* · Michael Bronstein <https://www.cs.ox.ac.uk/people/michael.bronstein/> University of Oxford UK · Kathleen Carley <https://www.cylab.cmu.edu/directory/bios/carley-kathleen.html> Carnegie Mellon University USA · Manlio De Domenico <https://manliodedomenico.com/> University of Padua Italy · Danai Koutra <http://web.eecs.umich.edu/~dkoutra/> University of Michigan USA · Romualdo Pastor-Satorras <http://www.fen.upc.edu/~romu/> Univ. Politècnica de Catalunya Spain · Tao Zhou <https://scholar.google.com/citations?user=MXgWgmEAAAAJ&hl=en> USTC China *TUTORIALS* · Tiago de Paula Peixoto <https://skewed.de/tiago> CEU Vienna Austria · Maria Liakata <https://www.turing.ac.uk/people/researchers/maria-liakata> Queen Mary University of London UK *PUBLICATION* Full papers (not previously published up to 12 pages) and Extended Abstracts (about published or unpublished research up to 4 pages) are welcome. · *Papers *will be included in the conference *proceedings edited by Springer* · *Extended abstracts* will be published in the *Book of Abstracts (with ISBN)* Extended versions will be invited for publication in *special issues of international journals:* o Applied Network Science <https://appliednetsci.springeropen.com/> edited by Springer o Advances in Complex Systems <https://www.worldscientific.com/toc/acs/24/02> edited by World Scientific o Complex Systems <https://www.complex-systems.com/about/> o Entropy <https://www.mdpi.com/journal/entropy> edited by MDPI o PLOS <https://journals.plos.org/plosone/> one <https://journals.plos.org/plosone/> o Social Network Analysis and Mining <https://link.springer.com/journal/13278> edited by Springer *SUBMISSION* https://complexnetworks.org/submission/ *TOPICS* *Topics include, but are not limited to: * o Models of Complex Networks o Structural Network Properties and Analysis o Epidemics o Community Structure o Motifs o Network Mining o Network embedding o Machine learning with graphs o Dynamics and Evolution Patterns of Complex Networks o Link Prediction o Multilayer Networks o Controllability o Synchronization o Visual Representation o Large-scale Graph Analytics o Social Reputation, Influence, and Trust o Information Spreading in Social Media o Rumour and Viral Marketing in Social Networks o Recommendation Systems o Financial and Economic Networks o Mobility o Biological and Technological Networks o Mobile call Networks o Bioinformatics o Earth Sciences Applications o Resilience and Robustness of Complex Networks o Networks for Physical Infrastructures o Networks, Smart Cities and Smart Grids o Political networks o Supply chain networks o Complex networks and information systems o Complex networks and CPS/IoT o Graph signal processing o Cognitive Network Science o Network Medicine o Network Neuroscience o Quantifying success through network analysis o Temporal and spatial networks o Historical Networks o Graph-Based Natural Language Processing *GENERAL CHAIRS* Hocine Cherifi (University of Burgundy, France) Luis M. Rocha (Binghamton University, USA) Join us at COMPLEX NETWORKS 2023 <https://www.complexnetworks.org/> *-------------------------* Hocine CHERIFI University of Burgundy Franche-Comté Laboratoire* I*nterdisciplinaire *C*arnot de *B*ourgogne - ICB UMR 6303 CNRS Editor in Chief Applied Network Science <https://appliednetsci.springeropen.com/> Plos Complex Systems <https://plos.org/complex-systems-research-journal/#:~:text=PLOS%20Complex%2….> Editorial Board member PLOS One <https://journals.plos.org/plosone/>, IEEE ACCESS <https://ieeeaccess.ieee.org/?http%3A%2F%2Fieeeaccess_ieee_org%2F>, Scientific Reports <https://www.nature.com/srep/>, Journal of Imaging <https://www.mdpi.com/journal/jimaging>, Quality and Quantity <https://www.springer.com/journal/11135/>, Computational Social Networks <https://computationalsocialnetworks.springeropen.com/>, Complex Systems <https://www.complex-systems.com/>

July 26, 2023