[{"status":"public","abstract":[{"text":"<jats:p>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 <jats:ext-link>https://github.com/crjimene/swarm_gpt</jats:ext-link>.</jats:p>","lang":"eng"}],"publication":"Frontiers in Artificial Intelligence","type":"journal_article","language":[{"iso":"eng"}],"article_number":"1593017","user_id":"117951","_id":"60897","intvolume":"         8","citation":{"apa":"Jimenez-Romero, C., Yegenoglu, A., &#38; Blum, C. (2025). Multi-agent systems powered by large language models: applications in swarm intelligence. <i>Frontiers in Artificial Intelligence</i>, <i>8</i>, Article 1593017. <a href=\"https://doi.org/10.3389/frai.2025.1593017\">https://doi.org/10.3389/frai.2025.1593017</a>","mla":"Jimenez-Romero, Cristian, et al. “Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence.” <i>Frontiers in Artificial Intelligence</i>, vol. 8, 1593017, Frontiers Media SA, 2025, doi:<a href=\"https://doi.org/10.3389/frai.2025.1593017\">10.3389/frai.2025.1593017</a>.","short":"C. Jimenez-Romero, A. Yegenoglu, C. Blum, Frontiers in Artificial Intelligence 8 (2025).","bibtex":"@article{Jimenez-Romero_Yegenoglu_Blum_2025, title={Multi-agent systems powered by large language models: applications in swarm intelligence}, volume={8}, DOI={<a href=\"https://doi.org/10.3389/frai.2025.1593017\">10.3389/frai.2025.1593017</a>}, number={1593017}, journal={Frontiers in Artificial Intelligence}, publisher={Frontiers Media SA}, author={Jimenez-Romero, Cristian and Yegenoglu, Alper and Blum, Christian}, year={2025} }","ama":"Jimenez-Romero C, Yegenoglu A, Blum C. Multi-agent systems powered by large language models: applications in swarm intelligence. <i>Frontiers in Artificial Intelligence</i>. 2025;8. doi:<a href=\"https://doi.org/10.3389/frai.2025.1593017\">10.3389/frai.2025.1593017</a>","chicago":"Jimenez-Romero, Cristian, Alper Yegenoglu, and Christian Blum. “Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence.” <i>Frontiers in Artificial Intelligence</i> 8 (2025). <a href=\"https://doi.org/10.3389/frai.2025.1593017\">https://doi.org/10.3389/frai.2025.1593017</a>.","ieee":"C. Jimenez-Romero, A. Yegenoglu, and C. Blum, “Multi-agent systems powered by large language models: applications in swarm intelligence,” <i>Frontiers in Artificial Intelligence</i>, vol. 8, Art. no. 1593017, 2025, doi: <a href=\"https://doi.org/10.3389/frai.2025.1593017\">10.3389/frai.2025.1593017</a>."},"year":"2025","publication_identifier":{"issn":["2624-8212"]},"publication_status":"published","doi":"10.3389/frai.2025.1593017","title":"Multi-agent systems powered by large language models: applications in swarm intelligence","volume":8,"date_created":"2025-08-06T14:59:29Z","author":[{"full_name":"Jimenez-Romero, Cristian","last_name":"Jimenez-Romero","first_name":"Cristian"},{"first_name":"Alper","orcid":"0000-0001-8869-215X","last_name":"Yegenoglu","full_name":"Yegenoglu, Alper","id":"117951"},{"first_name":"Christian","full_name":"Blum, Christian","last_name":"Blum"}],"publisher":"Frontiers Media SA","date_updated":"2025-08-08T11:37:53Z"},{"status":"public","abstract":[{"text":"<jats:title>Abstract</jats:title><jats:p>Neuroscience 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.</jats:p>","lang":"eng"}],"type":"journal_article","publication":"Neuroinformatics","language":[{"iso":"eng"}],"user_id":"117951","_id":"60898","citation":{"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. <i>Neuroinformatics</i>, <i>22</i>(4), 657–678. <a href=\"https://doi.org/10.1007/s12021-024-09682-6\">https://doi.org/10.1007/s12021-024-09682-6</a>","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={<a href=\"https://doi.org/10.1007/s12021-024-09682-6\">10.1007/s12021-024-09682-6</a>}, 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} }","mla":"Geminiani, Alice, et al. “Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.” <i>Neuroinformatics</i>, vol. 22, no. 4, Springer Science and Business Media LLC, 2024, pp. 657–78, doi:<a href=\"https://doi.org/10.1007/s12021-024-09682-6\">10.1007/s12021-024-09682-6</a>.","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.","ama":"Geminiani A, Kathrein J, Yegenoglu A, et al. Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. <i>Neuroinformatics</i>. 2024;22(4):657-678. doi:<a href=\"https://doi.org/10.1007/s12021-024-09682-6\">10.1007/s12021-024-09682-6</a>","ieee":"A. Geminiani <i>et al.</i>, “Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme,” <i>Neuroinformatics</i>, vol. 22, no. 4, pp. 657–678, 2024, doi: <a href=\"https://doi.org/10.1007/s12021-024-09682-6\">10.1007/s12021-024-09682-6</a>.","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.” <i>Neuroinformatics</i> 22, no. 4 (2024): 657–78. <a href=\"https://doi.org/10.1007/s12021-024-09682-6\">https://doi.org/10.1007/s12021-024-09682-6</a>."},"intvolume":"        22","page":"657-678","year":"2024","issue":"4","publication_status":"published","publication_identifier":{"issn":["1559-0089"]},"doi":"10.1007/s12021-024-09682-6","title":"Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme","author":[{"last_name":"Geminiani","full_name":"Geminiani, Alice","first_name":"Alice"},{"full_name":"Kathrein, Judith","last_name":"Kathrein","first_name":"Judith"},{"orcid":"0000-0001-8869-215X","last_name":"Yegenoglu","id":"117951","full_name":"Yegenoglu, Alper","first_name":"Alper"},{"first_name":"Franziska","last_name":"Vogel","full_name":"Vogel, Franziska"},{"first_name":"Marcelo","last_name":"Armendariz","full_name":"Armendariz, Marcelo"},{"last_name":"Ben-Zion","full_name":"Ben-Zion, Ziv","first_name":"Ziv"},{"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"},{"last_name":"Klijn","full_name":"Klijn, Wouter","first_name":"Wouter"},{"first_name":"Tina","last_name":"Kokan","full_name":"Kokan, Tina"},{"full_name":"Lupascu, Carmen Alina","last_name":"Lupascu","first_name":"Carmen Alina"},{"full_name":"Lührs, Anna","last_name":"Lührs","first_name":"Anna"},{"first_name":"Tara","last_name":"Mahfoud","full_name":"Mahfoud, Tara"},{"first_name":"Taylan","full_name":"Özden, Taylan","last_name":"Özden"},{"full_name":"Pedersen, Jens Egholm","last_name":"Pedersen","first_name":"Jens Egholm"},{"full_name":"Peres, Luca","last_name":"Peres","first_name":"Luca"},{"full_name":"Reiten, Ingrid","last_name":"Reiten","first_name":"Ingrid"},{"first_name":"Nikola","full_name":"Simidjievski, Nikola","last_name":"Simidjievski"},{"last_name":"Ulnicane","full_name":"Ulnicane, Inga","first_name":"Inga"},{"last_name":"van der Vlag","full_name":"van der Vlag, Michiel","first_name":"Michiel"},{"full_name":"Zehl, Lyuba","last_name":"Zehl","first_name":"Lyuba"},{"full_name":"Saria, Alois","last_name":"Saria","first_name":"Alois"},{"last_name":"Diaz-Pier","full_name":"Diaz-Pier, Sandra","first_name":"Sandra"},{"last_name":"Passecker","full_name":"Passecker, Johannes","first_name":"Johannes"}],"date_created":"2025-08-06T15:02:05Z","volume":22,"publisher":"Springer Science and Business Media LLC","date_updated":"2025-08-08T11:39:46Z"},{"publication":"Swarm Intelligence","type":"journal_article","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title><jats:p>Social 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</jats:p>"}],"status":"public","_id":"60899","user_id":"117951","language":[{"iso":"eng"}],"publication_identifier":{"issn":["1935-3812","1935-3820"]},"publication_status":"published","issue":"1","year":"2023","intvolume":"        18","page":"1-29","citation":{"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={<a href=\"https://doi.org/10.1007/s11721-023-00231-6\">10.1007/s11721-023-00231-6</a>}, 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} }","short":"C. Jimenez Romero, A. Yegenoglu, A. Pérez Martín, S. Diaz-Pier, A. Morrison, Swarm Intelligence 18 (2023) 1–29.","mla":"Jimenez Romero, Cristian, et al. “Emergent Communication Enhances Foraging Behavior in Evolved Swarms Controlled by Spiking Neural Networks.” <i>Swarm Intelligence</i>, vol. 18, no. 1, Springer Science and Business Media LLC, 2023, pp. 1–29, doi:<a href=\"https://doi.org/10.1007/s11721-023-00231-6\">10.1007/s11721-023-00231-6</a>.","apa":"Jimenez Romero, C., Yegenoglu, A., Pérez Martín, A., Diaz-Pier, S., &#38; Morrison, A. (2023). Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks. <i>Swarm Intelligence</i>, <i>18</i>(1), 1–29. <a href=\"https://doi.org/10.1007/s11721-023-00231-6\">https://doi.org/10.1007/s11721-023-00231-6</a>","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. <i>Swarm Intelligence</i>. 2023;18(1):1-29. doi:<a href=\"https://doi.org/10.1007/s11721-023-00231-6\">10.1007/s11721-023-00231-6</a>","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,” <i>Swarm Intelligence</i>, vol. 18, no. 1, pp. 1–29, 2023, doi: <a href=\"https://doi.org/10.1007/s11721-023-00231-6\">10.1007/s11721-023-00231-6</a>.","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.” <i>Swarm Intelligence</i> 18, no. 1 (2023): 1–29. <a href=\"https://doi.org/10.1007/s11721-023-00231-6\">https://doi.org/10.1007/s11721-023-00231-6</a>."},"date_updated":"2025-08-08T11:41:28Z","publisher":"Springer Science and Business Media LLC","volume":18,"author":[{"full_name":"Jimenez Romero, Cristian","last_name":"Jimenez Romero","first_name":"Cristian"},{"full_name":"Yegenoglu, Alper","id":"117951","last_name":"Yegenoglu","orcid":"0000-0001-8869-215X","first_name":"Alper"},{"first_name":"Aarón","last_name":"Pérez Martín","full_name":"Pérez Martín, Aarón"},{"last_name":"Diaz-Pier","full_name":"Diaz-Pier, Sandra","first_name":"Sandra"},{"last_name":"Morrison","full_name":"Morrison, Abigail","first_name":"Abigail"}],"date_created":"2025-08-06T15:02:25Z","title":"Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks","doi":"10.1007/s11721-023-00231-6"},{"user_id":"117951","_id":"60900","language":[{"iso":"eng"}],"article_number":"885207","publication":"Frontiers in Computational Neuroscience","type":"journal_article","status":"public","abstract":[{"lang":"eng","text":"<jats:p>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.</jats:p>"}],"volume":16,"author":[{"orcid":"0000-0001-8869-215X","last_name":"Yegenoglu","full_name":"Yegenoglu, Alper","id":"117951","first_name":"Alper"},{"last_name":"Subramoney","full_name":"Subramoney, Anand","first_name":"Anand"},{"last_name":"Hater","full_name":"Hater, Thorsten","first_name":"Thorsten"},{"full_name":"Jimenez-Romero, Cristian","last_name":"Jimenez-Romero","first_name":"Cristian"},{"first_name":"Wouter","last_name":"Klijn","full_name":"Klijn, Wouter"},{"full_name":"Pérez Martín, Aarón","last_name":"Pérez Martín","first_name":"Aarón"},{"last_name":"van der Vlag","full_name":"van der Vlag, Michiel","first_name":"Michiel"},{"last_name":"Herty","full_name":"Herty, Michael","first_name":"Michael"},{"first_name":"Abigail","full_name":"Morrison, Abigail","last_name":"Morrison"},{"last_name":"Diaz-Pier","full_name":"Diaz-Pier, Sandra","first_name":"Sandra"}],"date_created":"2025-08-06T15:02:30Z","date_updated":"2025-08-08T11:40:08Z","publisher":"Frontiers Media SA","doi":"10.3389/fncom.2022.885207","title":"Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn","publication_identifier":{"issn":["1662-5188"]},"publication_status":"published","intvolume":"        16","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. <i>Frontiers in Computational Neuroscience</i>. 2022;16. doi:<a href=\"https://doi.org/10.3389/fncom.2022.885207\">10.3389/fncom.2022.885207</a>","ieee":"A. Yegenoglu <i>et al.</i>, “Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn,” <i>Frontiers in Computational Neuroscience</i>, vol. 16, Art. no. 885207, 2022, doi: <a href=\"https://doi.org/10.3389/fncom.2022.885207\">10.3389/fncom.2022.885207</a>.","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.” <i>Frontiers in Computational Neuroscience</i> 16 (2022). <a href=\"https://doi.org/10.3389/fncom.2022.885207\">https://doi.org/10.3389/fncom.2022.885207</a>.","mla":"Yegenoglu, Alper, et al. “Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.” <i>Frontiers in Computational Neuroscience</i>, vol. 16, 885207, Frontiers Media SA, 2022, doi:<a href=\"https://doi.org/10.3389/fncom.2022.885207\">10.3389/fncom.2022.885207</a>.","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).","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={<a href=\"https://doi.org/10.3389/fncom.2022.885207\">10.3389/fncom.2022.885207</a>}, 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} }","apa":"Yegenoglu, A., Subramoney, A., Hater, T., Jimenez-Romero, C., Klijn, W., Pérez Martín, A., van der Vlag, M., Herty, M., Morrison, A., &#38; Diaz-Pier, S. (2022). Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn. <i>Frontiers in Computational Neuroscience</i>, <i>16</i>, Article 885207. <a href=\"https://doi.org/10.3389/fncom.2022.885207\">https://doi.org/10.3389/fncom.2022.885207</a>"},"year":"2022"},{"language":[{"iso":"eng"}],"_id":"60901","user_id":"117951","abstract":[{"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.","lang":"eng"}],"status":"public","publication":"Lecture Notes in Computer Science","type":"book_chapter","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","date_updated":"2025-08-08T11:36:59Z","publisher":"Springer International Publishing","date_created":"2025-08-06T15:02:38Z","author":[{"last_name":"Yegenoglu","orcid":"0000-0001-8869-215X","full_name":"Yegenoglu, Alper","id":"117951","first_name":"Alper"},{"last_name":"Krajsek","full_name":"Krajsek, Kai","first_name":"Kai"},{"first_name":"Sandra Diaz","full_name":"Pier, Sandra Diaz","last_name":"Pier"},{"first_name":"Michael","full_name":"Herty, Michael","last_name":"Herty"}],"year":"2021","place":"Cham","citation":{"short":"A. Yegenoglu, K. Krajsek, S.D. Pier, M. Herty, in: Lecture Notes in Computer Science, Springer International Publishing, Cham, 2021.","mla":"Yegenoglu, Alper, et al. “Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-Performing Gradient Descent.” <i>Lecture Notes in Computer Science</i>, Springer International Publishing, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-64580-9_7\">10.1007/978-3-030-64580-9_7</a>.","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={<a href=\"https://doi.org/10.1007/978-3-030-64580-9_7\">10.1007/978-3-030-64580-9_7</a>}, 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} }","apa":"Yegenoglu, A., Krajsek, K., Pier, S. D., &#38; Herty, M. (2021). Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent. In <i>Lecture Notes in Computer Science</i>. Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-64580-9_7\">https://doi.org/10.1007/978-3-030-64580-9_7</a>","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: <i>Lecture Notes in Computer Science</i>. Springer International Publishing; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-64580-9_7\">10.1007/978-3-030-64580-9_7</a>","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 <i>Lecture Notes in Computer Science</i>. Cham: Springer International Publishing, 2021. <a href=\"https://doi.org/10.1007/978-3-030-64580-9_7\">https://doi.org/10.1007/978-3-030-64580-9_7</a>.","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 <i>Lecture Notes in Computer Science</i>, Cham: Springer International Publishing, 2021."},"publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030645793","9783030645809"]},"publication_status":"published"},{"citation":{"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., &#38; Grün, S. (2017). A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC. In <i>Lecture Notes in Computer Science</i>. Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-319-53862-4_21\">https://doi.org/10.1007/978-3-319-53862-4_21</a>","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. 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Senk <i>et al.</i>, “A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC,” in <i>Lecture Notes in Computer Science</i>, Cham: Springer International Publishing, 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 <i>Lecture Notes in Computer Science</i>. Cham: Springer International Publishing, 2017. <a href=\"https://doi.org/10.1007/978-3-319-53862-4_21\">https://doi.org/10.1007/978-3-319-53862-4_21</a>.","ama":"Senk J, Yegenoglu A, Amblet O, et al. A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC. In: <i>Lecture Notes in Computer Science</i>. 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