Alper Yegenoglu

Profile

I'm a Research Scientist/postdoctoral researcher in Computational Neuroscience and Machine Learning working on neuromorphic computing, swarm intelligence and large language models. My work focuses on developing novel computational methods and tools for both artificial and biological neural networks. I have extensive experience in building software frameworks for parameter optimization and data analysis, particularly in the context of spiking neural networks and swarm intelligence. A key contribution has been the development of Learning to Learn (L2L), a framework for gradient-free optimization of neural networks using high-performance computing. I also have experience in computational methods for analyzing electrophysiological data.

More recently, I've expanded my research into integrating large language models with multi-agent systems and swarm intelligence applications. I'm particularly interested in how LLMs can be used to generate emergent behaviors in swarm systems and how bio-inspired computing approaches can be combined with modern AI techniques. Throughout my career, I've maintained a strong focus on developing open-source scientific software tools that enable reproducible research in computational neuroscience and machine learning.

Publications

Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence

Cristian Jimenez-Romero, Alper Yegenoglu, Christian Blum

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Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme

Alice Geminiani, Judith Kathrein, Alper Yegenoglu, Franziska Vogel, Marcelo Armendariz, Ziv Ben-Zion, Petruţ A. Bogdan, Joana Covelo, Marissa Diaz Pier, K. Grasenick, V. Karasenko, W. Klijn, Tina Kokan, C. Lupascu, Anna Lührs, T. Mahfoud, Taylan Özden, Jens Egholm Pedersen, Luca Peres, I. Reiten, Nikola Simidjievski, I. Ulnicane, Michiel van der Vlag, Lyuba Zehl, Alois Saria, Sandra Díaz-Pier, Johannes Passecker

Neuroinformatics 2024

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Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes

Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes

E. Pastorelli, Alper Yegenoglu, Nicole Kolodziej, Willem Wybo, F. Simula, Sandra Diaz, Johan F Storm, P. Paolucci

arXiv.org 2023

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Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks

Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks

Cristian Jimenez-Romero, Alper Yegenoglu, Aarón Pérez Martín, Sandra Díaz-Pier, A. Morrison

Swarm Intelligence 2022

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Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

Alper Yegenoglu, Anand Subramoney, T. Hater, Cristian Jimenez-Romero, W. Klijn, Aarn Pérez Martín, Michiel van der Vlag, M. Herty, A. Morrison, Sandra Díaz-Pier

Frontiers in Computational Neuroscience 2022

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Generalised learning of time-series: Ornstein-Uhlenbeck processes

Generalised learning of time-series: Ornstein-Uhlenbeck processes

Mehmet Süzen, Alper Yegenoglu

arXiv.org 2019

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Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

Pietro Quaglio, Alper Yegenoglu, Emiliano Torre, Dominik M. Endres, S. Grün

Frontiers in Computational Neuroscience 2017

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A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC

Johanna Senk, Alper Yegenoglu, O. Amblet, Yury Brukau, Andrew P. Davison, D. Lester, Anna Lührs, Pietro Quaglio, Vahid Rostami, Andrew Rowley, B. Schuller, A. B. Stokes, Sacha Jennifer van Albada, Daniel Zielasko, M. Diesmann, B. Weyers, M. Denker, S. Grün

JARA-HPC Symposium 2016

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Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains

Alper Yegenoglu, Pietro Quaglio, Emiliano Torre, S. Grün, Dominik M. Endres

International Conference on Conceptual Structures 2016

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Gradient-free optimization of artificial and biological networks using learning to learn

Alper Yegenoglu

4th HBP Student Conference

S. Diaz, L. Peres, A. Meegen, G. Urbain, Alper Yegenoglu, L. Saxer, C. Lupascu, Theresa Rass, Marta Turégano

Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent

Alper Yegenoglu, K. Krajsek, Sandra Díaz-Pier, M. Herty

International Conference on Machine Learning, Optimization, and Data Science 2020

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Using a Kalman filter as optimizer for L2L

Alper Yegenoglu

Reproducible data analysis of activity data using open-source software toolsNeo & Elephant

Julia Sprenger, M. Denker, Alper Yegenoglu, S. Grün

in Alzheimer's disease

Adam J. H. Newton, R. McDougal, W. Lytton, S. Dura-Bernal, Hermann Cuntz, Thomas Künzel, Alper Yegenoglu, Aaron Perez, Sandra Diaz, Michiel A. van der Vlag, Yanbo Lian, A. Burkitt, Alessio Marta, R. D. Schepper, E. D'Angelo, C. Casellato, Andrea Di, Ali Almasi, S. Bauquier, M. Renfree, H. Meffin, Young Jun Jung, Shi Sun, Molis Yunzab, M. Ibbotson, Martina Mittag, Alexander Bird, P. Jedlicka, Dirk Bernhardt-Walther, D. Farzanfar, Seohee Han, M. Rezanejad, Julian Schulte, Mario Senden, Gustavo Deco, X. Kobeleva, G. Zamora-López, Paul Züge, Christian Klos, Raoul-Martin Memmesheimer, Tuomo Mäki-Marttunen, Jan Fredrik Kismul, T. Manninen, M. Linne, Gaute T. Einevoll, O. Andreassen, J. Kotaleski, Kadri Pajo, Zahra Faghani, Catherine E. Carr, R. Kempter, Paula T. Kuokkanen, N. Meneghetti, T. V. Ness, A. Mazzoni, Jordan Culp, Wilten Nicola, Donovan M. Ashby, George W. Antis, Gordon Teskey, Alexander McGirr, Michael J. Tarlton, A. Yazidi, Gustavo B. M. Mello, Christopher K. Kovach, João Pedro, Carvalho Moreira, Joel Berger, Matthew A. Howard, Laura Gwilliams, Ariah Fallah, Lindy Comstock, Eduardo Mendes, Ankur Sinha, K. Obermayer, Volker Steuber, Christoph Metzner, Giulia Moreni, Tamas Fuzesi, Neilen P. Rasiah, Mijail Jaideep Bains, Rojas Carvajal, Taylor Chomiak, D. Rosenegger, Anupam Bisht, Kathryn Simone, Leonardo Molina, Gavin N Petrie, Matthew N Hill, K. Murari, Sundari Elango, S. Chakravarthy, Pratik K Mutha, Florian Unger, Jonathan Krebs, Michael G. Müller, Sanjay Ghosh, Ashish Raj, S. Nagarajan, Jeeyune Q. Jung, D. Doherty, S. Neymotin, L. Rubchinsky, Quynh-Anh Nguyen, T. Newton, James Knight, Thomas Nowotny, Fabian Schubert

Sharing Electrophysiological Data and Metadata on HBP Platforms – An Example Collaboratory Workflow

Julia Sprenger, M. Denker, Alper Yegenoglu, S. Grün

Parallel analysis codes for functional activity data analysis using the Elephant tool

C. Canova, S. Grün, M. Denker, Alper Yegenoglu, W. Klijn

Embedding the Elephant data analysis framework into a collaborative environment

M. Denker, Alper Yegenoglu, S. Grün

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ASSET for JULIA: Executing Massively Parallel Spike Correlation Analysis on KNL Cluster

W. Klijn, M. Denker, C. Canova, Alper Yegenoglu, P. Baumeister, S. Grün, D. Pleiter

SPADE: Spike Pattern Detection and Evaluation in Massively ParallelSpike Trains

Pietro Quaglio, Emiliano Torre, S. Grün, Alper Yegenoglu, Dominik M. Endres

Integrating HPC into a Collaborative Simulation-Analysis Workflow for Computational Neuroscience

Johanna Senk, M. Denker, Anna Lührs, O. Amblet, B. Weyers, D. Lester, S. Grün, Alper Yegenoglu, A. B. Stokes, Vahid Rostami, M. Diesmann, Yury Brukau, Andrew P. Davison, Daniel Zielasko, B. Schuller, Sacha Jennifer van Albada, Pietro Quaglio, Andrew Rowley

Elephant – Open-Source Tool for the Analysis of Electrophysiological Data Sets

Alper Yegenoglu, D. Holstein, Lon Phan, M. Denker, Andrew P. Davison, S. Grün