--- _id: '60897' abstract: - lang: eng text: 'This work examines the integration of large language models (LLMs) into multi-agent simulations by replacing the hard-coded programs of agents with LLM-driven prompts. The proposed approach is showcased in the context of two examples of complex systems from the field of swarm intelligence: ant colony foraging and bird flocking. Central to this study is a toolchain that integrates LLMs with the NetLogo simulation platform, leveraging its Python extension to enable communication with GPT-4o via the OpenAI API. This toolchain facilitates prompt-driven behavior generation, allowing agents to respond adaptively to environmental data. For both example applications mentioned above, we employ both structured, rule-based prompts and autonomous, knowledge-driven prompts. Our work demonstrates how this toolchain enables LLMs to study self-organizing processes and induce emergent behaviors within multi-agent environments, paving the way for new approaches to exploring intelligent systems and modeling swarm intelligence inspired by natural phenomena. We provide the code, including simulation files and data at https://github.com/crjimene/swarm_gpt.' article_number: '1593017' author: - first_name: Cristian full_name: Jimenez-Romero, Cristian last_name: Jimenez-Romero - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Christian full_name: Blum, Christian last_name: Blum citation: ama: 'Jimenez-Romero C, Yegenoglu A, Blum C. Multi-agent systems powered by large language models: applications in swarm intelligence. Frontiers in Artificial Intelligence. 2025;8. doi:10.3389/frai.2025.1593017' apa: 'Jimenez-Romero, C., Yegenoglu, A., & Blum, C. (2025). Multi-agent systems powered by large language models: applications in swarm intelligence. Frontiers in Artificial Intelligence, 8, Article 1593017. https://doi.org/10.3389/frai.2025.1593017' bibtex: '@article{Jimenez-Romero_Yegenoglu_Blum_2025, title={Multi-agent systems powered by large language models: applications in swarm intelligence}, volume={8}, DOI={10.3389/frai.2025.1593017}, number={1593017}, journal={Frontiers in Artificial Intelligence}, publisher={Frontiers Media SA}, author={Jimenez-Romero, Cristian and Yegenoglu, Alper and Blum, Christian}, year={2025} }' chicago: 'Jimenez-Romero, Cristian, Alper Yegenoglu, and Christian Blum. “Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence.” Frontiers in Artificial Intelligence 8 (2025). https://doi.org/10.3389/frai.2025.1593017.' ieee: 'C. Jimenez-Romero, A. Yegenoglu, and C. Blum, “Multi-agent systems powered by large language models: applications in swarm intelligence,” Frontiers in Artificial Intelligence, vol. 8, Art. no. 1593017, 2025, doi: 10.3389/frai.2025.1593017.' mla: 'Jimenez-Romero, Cristian, et al. “Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence.” Frontiers in Artificial Intelligence, vol. 8, 1593017, Frontiers Media SA, 2025, doi:10.3389/frai.2025.1593017.' short: C. Jimenez-Romero, A. Yegenoglu, C. Blum, Frontiers in Artificial Intelligence 8 (2025). date_created: 2025-08-06T14:59:29Z date_updated: 2025-08-08T11:37:53Z doi: 10.3389/frai.2025.1593017 intvolume: ' 8' language: - iso: eng publication: Frontiers in Artificial Intelligence publication_identifier: issn: - 2624-8212 publication_status: published publisher: Frontiers Media SA status: public title: 'Multi-agent systems powered by large language models: applications in swarm intelligence' type: journal_article user_id: '117951' volume: 8 year: '2025' ... --- _id: '60898' abstract: - lang: eng text: AbstractNeuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme’s approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme’s conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme. author: - first_name: Alice full_name: Geminiani, Alice last_name: Geminiani - first_name: Judith full_name: Kathrein, Judith last_name: Kathrein - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Franziska full_name: Vogel, Franziska last_name: Vogel - first_name: Marcelo full_name: Armendariz, Marcelo last_name: Armendariz - first_name: Ziv full_name: Ben-Zion, Ziv last_name: Ben-Zion - first_name: Petrut Antoniu full_name: Bogdan, Petrut Antoniu last_name: Bogdan - first_name: Joana full_name: Covelo, Joana last_name: Covelo - first_name: Marissa full_name: Diaz Pier, Marissa last_name: Diaz Pier - first_name: Karin full_name: Grasenick, Karin last_name: Grasenick - first_name: Vitali full_name: Karasenko, Vitali last_name: Karasenko - first_name: Wouter full_name: Klijn, Wouter last_name: Klijn - first_name: Tina full_name: Kokan, Tina last_name: Kokan - first_name: Carmen Alina full_name: Lupascu, Carmen Alina last_name: Lupascu - first_name: Anna full_name: Lührs, Anna last_name: Lührs - first_name: Tara full_name: Mahfoud, Tara last_name: Mahfoud - first_name: Taylan full_name: Özden, Taylan last_name: Özden - first_name: Jens Egholm full_name: Pedersen, Jens Egholm last_name: Pedersen - first_name: Luca full_name: Peres, Luca last_name: Peres - first_name: Ingrid full_name: Reiten, Ingrid last_name: Reiten - first_name: Nikola full_name: Simidjievski, Nikola last_name: Simidjievski - first_name: Inga full_name: Ulnicane, Inga last_name: Ulnicane - first_name: Michiel full_name: van der Vlag, Michiel last_name: van der Vlag - first_name: Lyuba full_name: Zehl, Lyuba last_name: Zehl - first_name: Alois full_name: Saria, Alois last_name: Saria - first_name: Sandra full_name: Diaz-Pier, Sandra last_name: Diaz-Pier - first_name: Johannes full_name: Passecker, Johannes last_name: Passecker citation: ama: 'Geminiani A, Kathrein J, Yegenoglu A, et al. Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. Neuroinformatics. 2024;22(4):657-678. doi:10.1007/s12021-024-09682-6' apa: 'Geminiani, A., Kathrein, J., Yegenoglu, A., Vogel, F., Armendariz, M., Ben-Zion, Z., Bogdan, P. A., Covelo, J., Diaz Pier, M., Grasenick, K., Karasenko, V., Klijn, W., Kokan, T., Lupascu, C. A., Lührs, A., Mahfoud, T., Özden, T., Pedersen, J. E., Peres, L., … Passecker, J. (2024). Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. Neuroinformatics, 22(4), 657–678. https://doi.org/10.1007/s12021-024-09682-6' bibtex: '@article{Geminiani_Kathrein_Yegenoglu_Vogel_Armendariz_Ben-Zion_Bogdan_Covelo_Diaz Pier_Grasenick_et al._2024, title={Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme}, volume={22}, DOI={10.1007/s12021-024-09682-6}, number={4}, journal={Neuroinformatics}, publisher={Springer Science and Business Media LLC}, author={Geminiani, Alice and Kathrein, Judith and Yegenoglu, Alper and Vogel, Franziska and Armendariz, Marcelo and Ben-Zion, Ziv and Bogdan, Petrut Antoniu and Covelo, Joana and Diaz Pier, Marissa and Grasenick, Karin and et al.}, year={2024}, pages={657–678} }' chicago: 'Geminiani, Alice, Judith Kathrein, Alper Yegenoglu, Franziska Vogel, Marcelo Armendariz, Ziv Ben-Zion, Petrut Antoniu Bogdan, et al. “Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.” Neuroinformatics 22, no. 4 (2024): 657–78. https://doi.org/10.1007/s12021-024-09682-6.' ieee: 'A. Geminiani et al., “Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme,” Neuroinformatics, vol. 22, no. 4, pp. 657–678, 2024, doi: 10.1007/s12021-024-09682-6.' mla: 'Geminiani, Alice, et al. “Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.” Neuroinformatics, vol. 22, no. 4, Springer Science and Business Media LLC, 2024, pp. 657–78, doi:10.1007/s12021-024-09682-6.' short: A. Geminiani, J. Kathrein, A. Yegenoglu, F. Vogel, M. Armendariz, Z. Ben-Zion, P.A. Bogdan, J. Covelo, M. Diaz Pier, K. Grasenick, V. Karasenko, W. Klijn, T. Kokan, C.A. Lupascu, A. Lührs, T. Mahfoud, T. Özden, J.E. Pedersen, L. Peres, I. Reiten, N. Simidjievski, I. Ulnicane, M. van der Vlag, L. Zehl, A. Saria, S. Diaz-Pier, J. Passecker, Neuroinformatics 22 (2024) 657–678. date_created: 2025-08-06T15:02:05Z date_updated: 2025-08-08T11:39:46Z doi: 10.1007/s12021-024-09682-6 intvolume: ' 22' issue: '4' language: - iso: eng page: 657-678 publication: Neuroinformatics publication_identifier: issn: - 1559-0089 publication_status: published publisher: Springer Science and Business Media LLC status: public title: 'Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme' type: journal_article user_id: '117951' volume: 22 year: '2024' ... --- _id: '60899' abstract: - lang: eng text: "AbstractSocial insects such as ants and termites communicate via pheromones which allow them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food or finding their way back to the nest. This behavior was shaped through evolutionary processes over millions of years. In computational models, self-coordination in swarms has been implemented using probabilistic or pre-defined simple action rules to shape the decision of each agent and the collective behavior. However, manual tuned decision rules may limit the emergent behavior of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any explicit rule. For this purpose, we evolve a swarm of agents representing an ant colony. We use an evolutionary algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behavior of each agent. The goal of the evolved colony is to find optimal ways to forage for food and return it to the nest in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide other ants. The pheromone usage is not manually encoded into the network; instead, this behavior is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication via pheromone did not emerge. Furthermore, we assess the foraging performance of the ant colonies by comparing the SNN-based model to a multi-agent rule-based system. Our results show that the SNN-based model can efficiently complete the foraging task in a short amount of time. Our approach illustrates that even in the absence of pre-defined rules, self-coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.\r\n" author: - first_name: Cristian full_name: Jimenez Romero, Cristian last_name: Jimenez Romero - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Aarón full_name: Pérez Martín, Aarón last_name: Pérez Martín - first_name: Sandra full_name: Diaz-Pier, Sandra last_name: Diaz-Pier - first_name: Abigail full_name: Morrison, Abigail last_name: Morrison citation: ama: Jimenez Romero C, Yegenoglu A, Pérez Martín A, Diaz-Pier S, Morrison A. Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks. Swarm Intelligence. 2023;18(1):1-29. doi:10.1007/s11721-023-00231-6 apa: Jimenez Romero, C., Yegenoglu, A., Pérez Martín, A., Diaz-Pier, S., & Morrison, A. (2023). Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks. Swarm Intelligence, 18(1), 1–29. https://doi.org/10.1007/s11721-023-00231-6 bibtex: '@article{Jimenez Romero_Yegenoglu_Pérez Martín_Diaz-Pier_Morrison_2023, title={Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks}, volume={18}, DOI={10.1007/s11721-023-00231-6}, number={1}, journal={Swarm Intelligence}, publisher={Springer Science and Business Media LLC}, author={Jimenez Romero, Cristian and Yegenoglu, Alper and Pérez Martín, Aarón and Diaz-Pier, Sandra and Morrison, Abigail}, year={2023}, pages={1–29} }' chicago: 'Jimenez Romero, Cristian, Alper Yegenoglu, Aarón Pérez Martín, Sandra Diaz-Pier, and Abigail Morrison. “Emergent Communication Enhances Foraging Behavior in Evolved Swarms Controlled by Spiking Neural Networks.” Swarm Intelligence 18, no. 1 (2023): 1–29. https://doi.org/10.1007/s11721-023-00231-6.' ieee: 'C. Jimenez Romero, A. Yegenoglu, A. Pérez Martín, S. Diaz-Pier, and A. Morrison, “Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks,” Swarm Intelligence, vol. 18, no. 1, pp. 1–29, 2023, doi: 10.1007/s11721-023-00231-6.' mla: Jimenez Romero, Cristian, et al. “Emergent Communication Enhances Foraging Behavior in Evolved Swarms Controlled by Spiking Neural Networks.” Swarm Intelligence, vol. 18, no. 1, Springer Science and Business Media LLC, 2023, pp. 1–29, doi:10.1007/s11721-023-00231-6. short: C. Jimenez Romero, A. Yegenoglu, A. Pérez Martín, S. Diaz-Pier, A. Morrison, Swarm Intelligence 18 (2023) 1–29. date_created: 2025-08-06T15:02:25Z date_updated: 2025-08-08T11:41:28Z doi: 10.1007/s11721-023-00231-6 intvolume: ' 18' issue: '1' language: - iso: eng page: 1-29 publication: Swarm Intelligence publication_identifier: issn: - 1935-3812 - 1935-3820 publication_status: published publisher: Springer Science and Business Media LLC status: public title: Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks type: journal_article user_id: '117951' volume: 18 year: '2023' ... --- _id: '60900' abstract: - lang: eng text: Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo. article_number: '885207' author: - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Anand full_name: Subramoney, Anand last_name: Subramoney - first_name: Thorsten full_name: Hater, Thorsten last_name: Hater - first_name: Cristian full_name: Jimenez-Romero, Cristian last_name: Jimenez-Romero - first_name: Wouter full_name: Klijn, Wouter last_name: Klijn - first_name: Aarón full_name: Pérez Martín, Aarón last_name: Pérez Martín - first_name: Michiel full_name: van der Vlag, Michiel last_name: van der Vlag - first_name: Michael full_name: Herty, Michael last_name: Herty - first_name: Abigail full_name: Morrison, Abigail last_name: Morrison - first_name: Sandra full_name: Diaz-Pier, Sandra last_name: Diaz-Pier citation: ama: Yegenoglu A, Subramoney A, Hater T, et al. Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn. Frontiers in Computational Neuroscience. 2022;16. doi:10.3389/fncom.2022.885207 apa: Yegenoglu, A., Subramoney, A., Hater, T., Jimenez-Romero, C., Klijn, W., Pérez Martín, A., van der Vlag, M., Herty, M., Morrison, A., & Diaz-Pier, S. (2022). Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn. Frontiers in Computational Neuroscience, 16, Article 885207. https://doi.org/10.3389/fncom.2022.885207 bibtex: '@article{Yegenoglu_Subramoney_Hater_Jimenez-Romero_Klijn_Pérez Martín_van der Vlag_Herty_Morrison_Diaz-Pier_2022, title={Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn}, volume={16}, DOI={10.3389/fncom.2022.885207}, number={885207}, journal={Frontiers in Computational Neuroscience}, publisher={Frontiers Media SA}, author={Yegenoglu, Alper and Subramoney, Anand and Hater, Thorsten and Jimenez-Romero, Cristian and Klijn, Wouter and Pérez Martín, Aarón and van der Vlag, Michiel and Herty, Michael and Morrison, Abigail and Diaz-Pier, Sandra}, year={2022} }' chicago: Yegenoglu, Alper, Anand Subramoney, Thorsten Hater, Cristian Jimenez-Romero, Wouter Klijn, Aarón Pérez Martín, Michiel van der Vlag, Michael Herty, Abigail Morrison, and Sandra Diaz-Pier. “Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.” Frontiers in Computational Neuroscience 16 (2022). https://doi.org/10.3389/fncom.2022.885207. ieee: 'A. Yegenoglu et al., “Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn,” Frontiers in Computational Neuroscience, vol. 16, Art. no. 885207, 2022, doi: 10.3389/fncom.2022.885207.' mla: Yegenoglu, Alper, et al. “Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.” Frontiers in Computational Neuroscience, vol. 16, 885207, Frontiers Media SA, 2022, doi:10.3389/fncom.2022.885207. short: A. Yegenoglu, A. Subramoney, T. Hater, C. Jimenez-Romero, W. Klijn, A. Pérez Martín, M. van der Vlag, M. Herty, A. Morrison, S. Diaz-Pier, Frontiers in Computational Neuroscience 16 (2022). date_created: 2025-08-06T15:02:30Z date_updated: 2025-08-08T11:40:08Z doi: 10.3389/fncom.2022.885207 intvolume: ' 16' language: - iso: eng publication: Frontiers in Computational Neuroscience publication_identifier: issn: - 1662-5188 publication_status: published publisher: Frontiers Media SA status: public title: Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn type: journal_article user_id: '117951' volume: 16 year: '2022' ... --- _id: '60901' abstract: - lang: eng text: The successful training of deep neural networks is dependent on initialization schemes and choice of activation functions. Non-optimally chosen parameter settings lead to the known problem of exploding or vanishing gradients. This issue occurs when gradient descent and backpropagation are applied. For this setting the Ensemble Kalman Filter (EnKF) can be used as an alternative optimizer when training neural networks. The EnKF does not require the explicit calculation of gradients or adjoints and we show this resolves the exploding and vanishing gradient problem. We analyze different parameter initializations, propose a dynamic change in ensembles and compare results to established methods. author: - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Kai full_name: Krajsek, Kai last_name: Krajsek - first_name: Sandra Diaz full_name: Pier, Sandra Diaz last_name: Pier - first_name: Michael full_name: Herty, Michael last_name: Herty citation: ama: 'Yegenoglu A, Krajsek K, Pier SD, Herty M. Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent. In: Lecture Notes in Computer Science. Springer International Publishing; 2021. doi:10.1007/978-3-030-64580-9_7' apa: 'Yegenoglu, A., Krajsek, K., Pier, S. D., & Herty, M. (2021). Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent. In Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-030-64580-9_7' bibtex: '@inbook{Yegenoglu_Krajsek_Pier_Herty_2021, place={Cham}, title={Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent}, DOI={10.1007/978-3-030-64580-9_7}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Yegenoglu, Alper and Krajsek, Kai and Pier, Sandra Diaz and Herty, Michael}, year={2021} }' chicago: 'Yegenoglu, Alper, Kai Krajsek, Sandra Diaz Pier, and Michael Herty. “Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-Performing Gradient Descent.” In Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-64580-9_7.' ieee: 'A. Yegenoglu, K. Krajsek, S. D. Pier, and M. Herty, “Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent,” in Lecture Notes in Computer Science, Cham: Springer International Publishing, 2021.' mla: 'Yegenoglu, Alper, et al. “Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-Performing Gradient Descent.” Lecture Notes in Computer Science, Springer International Publishing, 2021, doi:10.1007/978-3-030-64580-9_7.' short: 'A. Yegenoglu, K. Krajsek, S.D. Pier, M. Herty, in: Lecture Notes in Computer Science, Springer International Publishing, Cham, 2021.' date_created: 2025-08-06T15:02:38Z date_updated: 2025-08-08T11:36:59Z doi: 10.1007/978-3-030-64580-9_7 language: - iso: eng place: Cham publication: Lecture Notes in Computer Science publication_identifier: isbn: - '9783030645793' - '9783030645809' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer International Publishing status: public title: 'Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent' type: book_chapter user_id: '117951' year: '2021' ... --- _id: '60902' author: - first_name: Johanna full_name: Senk, Johanna last_name: Senk - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Olivier full_name: Amblet, Olivier last_name: Amblet - first_name: Yury full_name: Brukau, Yury last_name: Brukau - first_name: Andrew full_name: Davison, Andrew last_name: Davison - first_name: David Roland full_name: Lester, David Roland last_name: Lester - first_name: Anna full_name: Lührs, Anna last_name: Lührs - first_name: Pietro full_name: Quaglio, Pietro last_name: Quaglio - first_name: Vahid full_name: Rostami, Vahid last_name: Rostami - first_name: Andrew full_name: Rowley, Andrew last_name: Rowley - first_name: Bernd full_name: Schuller, Bernd last_name: Schuller - first_name: Alan Barry full_name: Stokes, Alan Barry last_name: Stokes - first_name: Sacha Jennifer full_name: van Albada, Sacha Jennifer last_name: van Albada - first_name: Daniel full_name: Zielasko, Daniel last_name: Zielasko - first_name: Markus full_name: Diesmann, Markus last_name: Diesmann - first_name: Benjamin full_name: Weyers, Benjamin last_name: Weyers - first_name: Michael full_name: Denker, Michael last_name: Denker - first_name: Sonja full_name: Grün, Sonja last_name: Grün citation: ama: 'Senk J, Yegenoglu A, Amblet O, et al. A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC. In: Lecture Notes in Computer Science. Springer International Publishing; 2017. doi:10.1007/978-3-319-53862-4_21' apa: Senk, J., Yegenoglu, A., Amblet, O., Brukau, Y., Davison, A., Lester, D. R., Lührs, A., Quaglio, P., Rostami, V., Rowley, A., Schuller, B., Stokes, A. B., van Albada, S. J., Zielasko, D., Diesmann, M., Weyers, B., Denker, M., & Grün, S. (2017). A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC. In Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-319-53862-4_21 bibtex: '@inbook{Senk_Yegenoglu_Amblet_Brukau_Davison_Lester_Lührs_Quaglio_Rostami_Rowley_et al._2017, place={Cham}, title={A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC}, DOI={10.1007/978-3-319-53862-4_21}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Senk, Johanna and Yegenoglu, Alper and Amblet, Olivier and Brukau, Yury and Davison, Andrew and Lester, David Roland and Lührs, Anna and Quaglio, Pietro and Rostami, Vahid and Rowley, Andrew and et al.}, year={2017} }' chicago: 'Senk, Johanna, Alper Yegenoglu, Olivier Amblet, Yury Brukau, Andrew Davison, David Roland Lester, Anna Lührs, et al. “A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC.” In Lecture Notes in Computer Science. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-53862-4_21.' ieee: 'J. Senk et al., “A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC,” in Lecture Notes in Computer Science, Cham: Springer International Publishing, 2017.' mla: Senk, Johanna, et al. “A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC.” Lecture Notes in Computer Science, Springer International Publishing, 2017, doi:10.1007/978-3-319-53862-4_21. short: 'J. Senk, A. Yegenoglu, O. Amblet, Y. Brukau, A. Davison, D.R. Lester, A. Lührs, P. Quaglio, V. Rostami, A. Rowley, B. Schuller, A.B. Stokes, S.J. van Albada, D. Zielasko, M. Diesmann, B. Weyers, M. Denker, S. Grün, in: Lecture Notes in Computer Science, Springer International Publishing, Cham, 2017.' date_created: 2025-08-06T15:02:46Z date_updated: 2025-08-08T11:39:32Z doi: 10.1007/978-3-319-53862-4_21 language: - iso: eng place: Cham publication: Lecture Notes in Computer Science publication_identifier: isbn: - '9783319538617' - '9783319538624' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer International Publishing status: public title: A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC type: book_chapter user_id: '117951' year: '2017' ... --- _id: '60903' article_number: '41' author: - first_name: Pietro full_name: Quaglio, Pietro last_name: Quaglio - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Emiliano full_name: Torre, Emiliano last_name: Torre - first_name: Dominik M. full_name: Endres, Dominik M. last_name: Endres - first_name: Sonja full_name: Grün, Sonja last_name: Grün citation: ama: Quaglio P, Yegenoglu A, Torre E, Endres DM, Grün S. Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE. Frontiers in Computational Neuroscience. 2017;11. doi:10.3389/fncom.2017.00041 apa: Quaglio, P., Yegenoglu, A., Torre, E., Endres, D. M., & Grün, S. (2017). Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE. Frontiers in Computational Neuroscience, 11, Article 41. https://doi.org/10.3389/fncom.2017.00041 bibtex: '@article{Quaglio_Yegenoglu_Torre_Endres_Grün_2017, title={Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE}, volume={11}, DOI={10.3389/fncom.2017.00041}, number={41}, journal={Frontiers in Computational Neuroscience}, publisher={Frontiers Media SA}, author={Quaglio, Pietro and Yegenoglu, Alper and Torre, Emiliano and Endres, Dominik M. and Grün, Sonja}, year={2017} }' chicago: Quaglio, Pietro, Alper Yegenoglu, Emiliano Torre, Dominik M. Endres, and Sonja Grün. “Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.” Frontiers in Computational Neuroscience 11 (2017). https://doi.org/10.3389/fncom.2017.00041. ieee: 'P. Quaglio, A. Yegenoglu, E. Torre, D. M. Endres, and S. Grün, “Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE,” Frontiers in Computational Neuroscience, vol. 11, Art. no. 41, 2017, doi: 10.3389/fncom.2017.00041.' mla: Quaglio, Pietro, et al. “Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.” Frontiers in Computational Neuroscience, vol. 11, 41, Frontiers Media SA, 2017, doi:10.3389/fncom.2017.00041. short: P. Quaglio, A. Yegenoglu, E. Torre, D.M. Endres, S. Grün, Frontiers in Computational Neuroscience 11 (2017). date_created: 2025-08-06T15:02:58Z date_updated: 2025-08-08T11:39:26Z doi: 10.3389/fncom.2017.00041 intvolume: ' 11' language: - iso: eng publication: Frontiers in Computational Neuroscience publication_identifier: issn: - 1662-5188 publication_status: published publisher: Frontiers Media SA status: public title: Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE type: journal_article user_id: '117951' volume: 11 year: '2017' ... --- _id: '60904' author: - first_name: Alper full_name: Yegenoglu, Alper id: '117951' last_name: Yegenoglu orcid: 0000-0001-8869-215X - first_name: Pietro full_name: Quaglio, Pietro last_name: Quaglio - first_name: Emiliano full_name: Torre, Emiliano last_name: Torre - first_name: Sonja full_name: Grün, Sonja last_name: Grün - first_name: Dominik full_name: Endres, Dominik last_name: Endres citation: ama: 'Yegenoglu A, Quaglio P, Torre E, Grün S, Endres D. Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains. In: Lecture Notes in Computer Science. Springer International Publishing; 2016. doi:10.1007/978-3-319-40985-6_1' apa: Yegenoglu, A., Quaglio, P., Torre, E., Grün, S., & Endres, D. (2016). Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains. In Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-319-40985-6_1 bibtex: '@inbook{Yegenoglu_Quaglio_Torre_Grün_Endres_2016, place={Cham}, title={Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains}, DOI={10.1007/978-3-319-40985-6_1}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Yegenoglu, Alper and Quaglio, Pietro and Torre, Emiliano and Grün, Sonja and Endres, Dominik}, year={2016} }' chicago: 'Yegenoglu, Alper, Pietro Quaglio, Emiliano Torre, Sonja Grün, and Dominik Endres. “Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-Temporal Spike Patterns in Massively Parallel Spike Trains.” In Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-40985-6_1.' ieee: 'A. Yegenoglu, P. Quaglio, E. Torre, S. Grün, and D. Endres, “Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains,” in Lecture Notes in Computer Science, Cham: Springer International Publishing, 2016.' mla: Yegenoglu, Alper, et al. “Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-Temporal Spike Patterns in Massively Parallel Spike Trains.” Lecture Notes in Computer Science, Springer International Publishing, 2016, doi:10.1007/978-3-319-40985-6_1. short: 'A. Yegenoglu, P. Quaglio, E. Torre, S. Grün, D. Endres, in: Lecture Notes in Computer Science, Springer International Publishing, Cham, 2016.' date_created: 2025-08-06T15:03:16Z date_updated: 2025-08-08T11:38:15Z doi: 10.1007/978-3-319-40985-6_1 language: - iso: eng place: Cham publication: Lecture Notes in Computer Science publication_identifier: isbn: - '9783319409849' - '9783319409856' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer International Publishing status: public title: Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains type: book_chapter user_id: '117951' year: '2016' ...