NEST Conference 2025 Contributions

NEST Conference 2025 NEST Initiative 17-18 June 2025 CC-BY-NC-4.0 The Virtual NEST Conference 2025 The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the virtual NEST Conference 2025. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. We particularly encourage young scientists to participate in the conference! The NEST Conference 2025 will be held as a virtual event on Tuesday & Wednesday, 17-18 June 2025 Please always check https://nest-simulator.org/conference for additional materials and continued updates of the program. For later reference this conference will stay available as https://nest-simulator.org/conference-2025. Recordings The sessions will not be recorded. We may take a few screen shots as “group photo” for this year. These will be announced several minutes in advance so you have a chance to either participate or opt-out. Assistance If you experience technical problems, please contact us on the Mattermost or send a mail to [email protected] with information about how we can contact you. 2 Schedule The latest schedule is available online. The following table lists times in CEST (UTC+2). Day 1, Tuesday 2025-06-17 3 Day 2, Wednesday 2025-06-18 4 Contributions Below you see the full list of contributions per day. Note that the schedule contains additional organizational parts,breaks and mingling sessions. The sections on the following pages provide details to each specific contribution and are sorted by contribution type. Day 1 ID Description 09:15 K-1 Full-scale point neuron models of mouse and human hippocampal microcircuits Daniela Gandolfi (Keynote, 45 min) 10:00 T-1 Adult Neurogenesis in the Dentate Gyrus Aadhar Sharma (Talk, 20 min) 10:20 T-2 A Graph-Based, In-Memory Workflow Library for Brain/MINDS 2.0 – The Digital Brain Project Carlos Gutierrez (Talk, 20 min) 10:40 T-3 Accelerated cortical microcircuit simulations on massively distributed memory Catherine Mia Schöfmann (Talk, 20 min) 11:15 W-1 Using NESTML and NEAT to create massively parallel network simulations with dendritic subunits Leander Ewert and Willem Wybo (Workshop, 90 min) 13:45 W-2 Power Spectral Analysis of NEST Simulation Using Neo and Elephant Michael Denker (Workshop, 90 min) 15:30 K-2 The SpiNNaker2 neuromorphic system for large-scale brain simulation and energy-efficient AI Bernhard Vogginger (Keynote, 45 min) 5 Day 2 ID Description 09:00 K-3 Signal propagation and denoising through topographic modularity of neural circuits Barna Zajzon (Keynote, 45 min) 09:45 W-3 Rapid prototyping in spiking neural network modeling with NESTML and NEST Desktop Charl Linssen and Pooja Babu (Workshop, 90 min) 11:30 T-4 PyNEST NG: A new modern interface to the NEST kernel Hans Ekkehard Plesser (Talk, 20 min) 11:50 T-5 A NEST-based framework for the parallel simulation of networks of compartmental models with customizable subcellular dynamics Leander Ewert and Willem Wybo (Talk, 20 min) 12:10 T-6 Modeling Calcium-Mediated Spike-Timing Dependent Plasticity in Spiking Neural Networks Carlo Andrea Sartori (Talk, 20 min) 13:30 T-7 Towards an empirically based model of the prefrontal cortex using AdEx neurons and NEST Pedro Ribeiro Pinheiro (Talk, 20 min) 13:50 K-4 Astroglia in Neural Circuits: Modeling Synaptic and Network Modulation Marja-Leena Linne (Keynote, 45 min) 14:45 P-1 Mitigating Catastrophic Forgetting in Biologically Plausible Learning Jesus Andres Espinoza Valverde (Poster & advertisement flash talk, 2 min) 14:47 P-2 ATP Restriction Influences Outcomes in Brain-in-a-Dish Models Utilizing EDLIF and ED-STDP Matias Urrea (Poster & advertisement flash talk, 2 min) 14:49 P-3 Continuous benchmarking of brain-research simulation code: Keeping pace with an evolving ecosystem of models and technologies Melissa Lober (Poster & advertisement flash talk, 2 min) 14:51 P-4 Towards a time-scale specific subspace for brain inter-area communication Daniel Moreno Soto (Poster & advertisement flash talk, 2 min) 14:53 P-5 A 3D Liquid Hippocampus Model for Memory Replay and Learning Xiao Shao (Poster & advertisement flash talk, 2 min) 14:55 P-6 Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model Han Lu (Poster & advertisement flash talk, 2 min) 6 Keynotes K-1 – Full-scale point neuron models of mouse and human hippocampal microcircuits Daniela Gandolfi,University of Modena and Reggio Emilia Authors: Daniela Gandolfi, Giulia Maria Boiani, Jonathan Mapelli, Lorenzo Tartarini, Michele Migliore, Sergio Solinas Keywords: hippocampus, network connectivity, synaptic plasticity The mathematical modeling of extended brain microcircuits is becoming an effective tool to simulate the neurophysiological correlates of brain activity while opening new perspectives in understanding the mechanisms underlying brain dysfunctions. The generation of realistic networks is however experiencing limitations due to the strategy adopted to build network connectivity and also to the computational cost associated with biophysically detailed neuronal models. We have recently developed a method to generate neuronal network scaffolds associating geometrical probability volumes with preand postsynaptic neurites. In this talk, I will show that the proposed approach allows to generate neuronal networks with realistic connectivity properties without the explicit use of 3D morphological reconstructions to be adopted for highly efficient simulation through point-like neuron models. The method has been benchmarked both on the mouse and human hippocampus CA1 region and its efficiency at different spatial scales has been explored. The abstract geometric reconstruction of axonal and dendritic occupancy, by effectively reflecting morphological and anatomical constraints, could be integrated into structured simulators generating entire circuits of different brain areas. References Gandolfi D, Mapelli J, Solinas S.M.G. et al. A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits. Sci Rep. 2022 Aug 16;12(1):13864. doi: 10.1038/s41598-022-18024-y. Erratum in: Sci Rep. 2022 Nov 17;12(1):19792. PMID: 35974119; PMCID: PMC9381785. Gandolfi, D., Mapelli, J., Solinas, S.M.G. et al. Full-scale scaffold model of the human hippocampus CA1 area. Nat Comput Sci 3, 264–276 (2023). https://doi.org/10.1038/s43588-023-00417-2. 7 K-2 – The SpiNNaker2 neuromorphic system for large-scale brain simulation and energy-efficient AI Bernhard Vogginger,TU Dresden Authors: Bernhard Vogginger Keywords: SpiNNaker2, Neuromorphic Hardware, Brain Simulation, Spiking Neural Networks TU Dresden has recently completed the construction of the world’s largest brain-inspired supercomputer „SpiNNcloud“, which will allow the real-time simulation of up to 5 billion neurons. It is based on the neuromorphic “SpiNNaker2” chip featuring 152 low-power ARM cores and digital accelerators to speed up the processing of spiking and deep neural networks. The overall system comprises a scalable communication infrastructure enabling the low-latency multi-cast routing of event packets such as spikes. All of this makes SpiNNaker2 a unique platform to explore largescale brain simulation and neuroinspired AI algorithms. This talk will introduce the SpiNNaker2 chip and system architecture and explain how spiking neural networks are simulated in real-time. Further, we will show examples of using SpiNNaker2 for efficient event-based AI processing. Finally, we will provide an overview of the software stack (including efforts on supporting NESTML) and give an outlook on the access options for SpiNNaker2. Acknowledgements This work is partly supported by the following projects: EBRAINS 2.0, ESCADE, ScaDS.AI. We’d like to thank all people involved in the SpiNNaker2 hardware and software development from TU Dresden, University of Manchester, and SpiNNcloud Systems. 8 K-3 – Signal propagation and denoising through topographic modularity of neural circuits Barna Zajzon,IAS-6, Forschungszentrum Jülich Authors: Barna Zajzon, Abigail Morrison, David Dahmen, Renato Duarte Keywords: Information transfer, modular networks, topographic maps, denoising To navigate a noisy and dynamic environment, the brain must form reliable representations from ambiguous sensory inputs. Since only information that successfully propagates through cortical hierarchies can influence perception and decision-making, preserving signal fidelity across processing stages is essential. This work investigates whether topographic maps — characterized by stimulus-specific pathways that preserve the relative organization of neuronal populations — may serve as a structural scaffold for robust transmission of sensory signals. Using a large modular circuit of spiking neurons comprising multiple sub-networks, we show that topographic projections are crucial for accurate propagation of stimulus representations. These projections not only maintain representational fidelity but also help the network suppress sensory and intrinsic noise. As input signals pass through the network, topographic precision regulates local E/I balance and effective connectivity, leading to gradual enhancement of internal representations and increased signal-to-noise ratio. This denoising effect emerges beyond a critical threshold in the sharpness of feedforward connections, giving rise to inhibitiondominated regimes where responses along stimulated pathways are selectively amplified. Our results indicate that this phenomenon is robust and generalizable, largely independent of model specifics. Through mean-field analysis, we further demonstrate that topographic modularity acts as a bifurcation parameter that controls the macroscopic dynamics, and that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. These findings suggest that topographic maps may universally support denoising and selective routing in neural systems, enabling behaviorally relevant regimes such as stable multi-stimulus representations, winner-take-all selection, and metastable dynamics associated with flexible behavior. References Zajzon, B., Dahmen, D., Morrison, A., & Duarte, R. (2023). Signal denoising through topographic modularity of neural circuits. Elife, 12, e77009. Acknowledgements This work has received partial support from the the Initiative and Networking Fund of the Helmholtz Association, the Helmholtz Portfolio theme Supercomputing and Modeling for the Human Brain, and the Excellence Initiative of the German federal and state governments (G:(DE-82)EXS-SF-neuroIC002). The authors gratefully acknowledge the computing time granted by the JARA-HPC Vergabegremium on the supercomputer JURECA at Forschungszentrum Jülich. 9 P-6 – Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model Han Lu,Institute for Advanced Simulation (IAS), Jülich Supercomputing Center, Forschungszentrum Jülich Authors: Han Lu, Lukas Frase, Claus Normann, Stefan Rotter Keywords: tDCS, motor learning, homeostatic structural plasticity, spiking neural network, cell assembly Transcranial direct current stimulation (tDCS) is increasingly used to modulate motor learning and rehabilitation, yet its effects vary depending on polarity, intensity, electrode montage, and timing. Traditional models based on Hebbian or homeostatic plasticity only partially explain these inconsistencies. We propose that homeostatic structural plasticity (HSP) can offer a more comprehensive explanation. Using a spiking neural network model governed by HSP, we simulated motor learning alongside different tDCS protocols. Our results show that the timing and spatial targeting of tDCS critically determine its impact on motor learning. tDCS applied before learning had minimal effects, slightly reducing connectivity when applied uniformly. During learning, targeted anodal stimulation enhanced engram connectivity, while cathodal or uniform stimulation weakened it. When applied after learning, only targeted cathodal tDCS increased engram strength. Non-specific or strong stimulation tended to distort engram structure. These findings suggest that both facilitatory and inhibitory effects of tDCS observed in human studies can be explained through changes in engram connectivity under HSP. Our model provides a mechanistic framework for understanding how tDCS modulates memory formation depending on application parameters, potentially guiding more effective and individualized stimulation protocols. Acknowledgements Additional support by the German Research Foundation (DFG) through EXC 1086, and by the state of BadenW¨urttemberg through bwHPC and the German Research Foundation (DFG) through INST 39/963-1 FUGG is acknowledged. The authors thank Julia V. Gallinaro for establishing the fundamental HSP model. We also thank Uwe Grauer from the Bernstein Center Freiburg, as well as Bernd Wiebelt and Michael Janczyk from the Freiburg University Computing Center for their assistance with HPC applications. 16 Talks T-1 – Adult Neurogenesis in the Dentate Gyrus Aadhar Sharma,Bernstein Center Freiburg, University of Freiburg Authors: Aadhar Sharma, Stefan Rotter Keywords: Adult Neurogenesis, Dentate Gyrus, Structural Plasticity As a rule, no new neurons are born in the adult mammalian brain. As an exception, however, adult neurogenesis is readily observed in niches such as the dentate gyrus where neuronal precursors are transformed into granule cells. Dentate granule cells constitute a major population of principal cells in the trisynaptic circuit and are implicated in hippocampal functions such as pattern separation. Newborn granule cells exhibit distinctive properties that, as they mature, progressively converge toward those of pre-established mature granule cells. While it has been suspected that the age-dependent properties of adult-born cells contribute to their integration into the network, it is not known how exactly the integration dynamics and hippocampal function are affected by it. We have developed models where adult-born granule cells undergo experimentally-matched maturation, forming new connections within the pre-existing networks. Our findings suggest that the age-dependent properties are critical for network integration. Analysis suggests that, if large numbers of cells are rapidly added, pathological states resembling epilepsy may emerge. Studying different network configurations further indicates that adult-born neurons compete for synaptic resources with mature ones, consistent with the experimental observation that perforant pathway synapses are redistributed between newborn and mature cells. Our models provide a promising tool to study the dynamics of adult neurogenesis and how it might affect hippocampal computations. In the talk, I will provide an overview of our modelling approach, and briefly summarise our preliminary results. I will also share the details about our latest model and the practical challenges involved with it. References 1. G. Kempermann et al., Cell Stem Cell, (2018), 23, 25–30, issn: 1934-5909 2. E. P. Moreno-Jiménez et al., Nature Medicine, (2019), 25, 554–560, issn: 1078-8956 3. J. T. Gonçalves, S. T. Schafer, F. H. Gage, Cell, (2016), 167, 897– 914, issn: 0092-8674 4. C. Schmidt-Hieber, P. Jonas, J. Bischofberger, Nature, (2004), 429, 184–187, issn: 0028-0836 (2004) 5. S. M. Miller, A. Sahay, Nature Neuroscience, (2019), 22, 1565–1575, issn: 1097-6256 6. L. Li, S. Sultan, S. Heigele, C. Schmidt-Salzmann, N. Toni, J. Bischofberger, eLife, (2017), 6, e23612 7. S. Diaz-Pier, M. Naveau, M. ButzOstendorf, A. Morrison, Frontiers in Neuroanatomy, (2016) 8. E. W. Adlaf et al., eLife, (2017), 6, e19886 (2017) 17 T-2 – A Graph-Based, In-Memory Workflow Library for Brain/MINDS 2.0 – The Digital Brain Project Carlos Gutierrez,Okinawa Institute of Science and Technology / SoftBank Authors: Carlos Gutierrez, Henrik Skibbe, Kenji Doya Keywords: brain simulation workflow The Brain/MINDS 2.0 Digital Brain Project aims to develop an open, interoperable software platform dedicated to digital brain construction. This platform targets seamless integration of neuroscience simulation tools—including TVB, NEST, BMTK, —via their Python APIs, allowing researchers to build comprehensive and detailed brain models. As brain modeling and simulation research evolves, there is a growing need for digital infrastructures that can efficiently handle heterogeneous data while supporting scalable simulation workflows. Current workflow systems like Snakemake and Nextflow are well-suited for linear dataprocessing pipelines but are inherently dependent on serialized, I/O-bound data exchanges. This makes them less effective for in-memory data structures typical of neural simulations, posing challenges for constructing understandable and reusable workflow modules. To address this gap, we introduce a graph-based workflow framework where brain modeling tasks are encapsulated as modular, reusable nodes. These nodes communicate using direct memory references, enabling rapid in-memory propagation of complex neural data (e.g., neuron states, connectivity matrices) and eliminating the overhead of serialization and disk I/O. Workflows are defined as node-edge graphs, fostering flexible, composable scientific pipelines. In addition, to leverage the benefits of traditional tools, our implementation analyzes data exchange patterns to identify optimal process boundaries, grouping tightly coupled in-memory tasks while enabling file-based I/O modularity where appropriate. This hybrid model aims to support distributed workflow execution with Nextflow and Snakemake under the hood, while maintaining the modularity, clarity, ease of use, and reusability needed for scientific users and collaborative research. Acknowledgements This work was supported by Brain/MINDS 2.0 project (AMED), Development of the“digital brain”and related research platforms utilizing mathematical models. 18 T-3 – Accelerated cortical microcircuit simulations on massively distributed memory Catherine Mia Schöfmann,PGI-15 Authors: Catherine Mia Schöfmann, Jan Finkbeiner, Susanne Kunkel Keywords: microcircuit Comprehensive simulation studies of dynamical regimes of cortical networks with realistic synaptic densities depend on compute systems capable of running such models significantly faster than biological real time. Since CPUs still are the primary target for established simulators, an inherent bottleneck caused by the von Neumann design is frequent memory access with minimal compute. Distributed memory architectures, popularized by the need for massively parallel and scalable processing for ML, offer an alternative. We introduce extensible simulation technology for spiking networks on massively distributed memory using Graphcore’s IPUs. We demonstrate the efficiency of the new technology based on simulations of the microcircuit model by (Potjans et al., 2014) commonly used as a reference benchmark. It represents 1~mm² of cortical tissue, and is considered a building block of cortical function. We present a custom communication algorithm especially suited for distributed and constrained memory environments, which allows a controlled trade-off between performance and memory usage. Our simulation code achieves an acceleration factor of 15x compared to real time for the full-scale cortical microcircuit model on the smallest device configuration capable of fitting the model in memory. This is competitive with the current record on a static FPGA cluster (Kauth et al., 2023), and further speedup can be achieved at the cost of lower precision. With negligible compilation times, the simulation code can be be extended seamlessly to a wide range of synapse and neuron models, as well as structural plasticity, unlocking a new class of models for extensive parameter-space explorations in computational neuroscience. References Potjans, T. C. and Diesmann, M. (2012). The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. Cerebral Cortex, 24(3):785–806. Kauth, K., Stadtmann, T., Sobhani, V., and Gemmeke, T. (2023). neuroaixframework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time. Frontiers in Computational Neuroscience, 17:1144143. Acknowledgements The presented conceptual and algorithmic work is part of our long-term collaborative project to provide the technology for neural systems simulations (https://www.nest-initiative.org). This research used resources of the Argonne Leadership Computing Facility, a U.S. Department of Energy (DOE) Office of Science user facility at Argonne National Laboratory and is based on research supported by the U.S. DOE Office of Science-Advanced Scientific Computing Research Program, under Contract No. DE-AC02-06CH11357. This work is partly funded by Volkswagen Foundation. 19 T-4 – PyNEST NG: A new modern interface to the NEST kernel Hans Ekkehard Plesser,Norwegian University of Life Sciences Authors: Hans Ekkehard Plesser, Håkon Mørk, Nicolai Haug, Jochen Martin Eppler SLI, the Simulation Language Interpreter, has been a defining feature of NEST since its beginning and its primary user interface [1]. Since the introduction of PyNEST with the first public beta of NEST 2.0 in 2008 [2], users have increasingly switched from SLI to PyNEST, to a point where today only few users and developers are fluent in SLI or familiar with the interpreters C++- implementation. We thus decided several years ago to remove the SLI interpreter from the NEST simulator and connect PyNEST directly with a NEST C++ API. A key challenge to removing the SLI interpreter from NEST was the DictionaryDatum data structure provided by SLI. It is central to data exchange between user and simulator and thus pervades much of NEST kernel and model code. As early as 2018, Jochen Eppler and Håkon Mørk began work on a “SLI-free” NEST, including a replacement for the SLI dictionary data structure. Nicolai Haug and Hans Ekkehard Plesser contributed at later stages. One of the last stumbling blocks to be resolved was an efficient approach to dictionary access checks, which is crucial to detect misspelled parameter names. The other major challenge in removing the SLI interpreter was to port the hundreds of tests for NEST written in SLI to PyTest. This was undertaken as a broad community effort during several hackathons and is not yet entirely completed. This work is done in the NEST master branch to immediately make the ported tests available. At present, PyNEST NG is essentially complete, with benchmark tests on various complex models indicating good, for some models notably reduced, model construction times. Once the remaining test will have been ported from SLI to PyTest, PyNEST NG will be ready for integration into NEST. This will only minimally affect the user interface. In my talk, I will give an overview over the implementation of NESTs new Python interface, benefits for users and developers, and discuss challenges ahead. ### Acknowledgements We are grateful to our many colleagues in the NEST developer community who contributed to the work towards PyNEST NG, in particular by porting tests, and to Renan Shimoura for benchmarking PyNEST NG against the clustered MAM model and to Markus Diesmann for co-piloting a major merge of the master branch into PyNEST NG after 15 months of inactivity. Research reported here was supported by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3). ### References [1] Diesmann, M., Gewaltig, M.-O., & Aertsen, A. (1995). SYNOD: An Environment for Neural Systems Simulation—Language Interface and Tutorial (Technical Report GC-AA-/95-3; p. 72). Weizman Institute of Science. [2] Eppler, J. M., Helias, M., Muller, E., Diesmann, M., & Gewaltig, M.-O. (2008). PyNEST: A convenient interface to the NEST simulator. Front Neuroinformatics, 2, 12. https://doi.org/10.3389/neuro.11.012.2008 20 T-5 – A NEST-based framework for the parallel simulation of networks of compartmental models with customizable subcellular dynamics Leander Ewert,IAS-6 Forschungszentrum Jülich and Willem Wybo,Forschungszentrum Jülich Authors: Abigail Morrison, Charl Linssen, Christophe Blaszyc, Jakob Jordan, Leander Ewert, Pooja Babu, Willem Wybo Keywords: multi-compartment models, dendritic dynamics, massively parallel networks The human brain computes in a massively parallel fashion, not only at the level of the neurons, but also through complex subcellular signaling networks which support learning and memory. Therefore, it’s desirable to utilize the parallelization capabilities of modern supercomputers to simulate the brain in a massively parallel fashion. The NEural Simulation Tool (NEST) [1] enables massively parallel spiking network simulations, being optimized to efficiently communicate spikes across MPI processes [2]. However, so far, NEST had limited options to simulate subcellular processes as part of the network. We have extended the NESTML modeling language [3] to support multi-compartment models, featuring user-specified dynamical processes (Fig 1AC). These dynamics are compiled into NEST models, which optimally leverage CPU vectorization. This adds a deeper, subcellular level of parallelization, allowing individual cores to parallelize multiple compartments. Furthermore, we leverage the Hines algorithm [4] to achieve stable and efficient integration of the system. Overall we gain single-neuron speedups compared to the field-standard NEURON simulator [5] of up to a factor of four to five (Fig 1D). Thus, we enable embedding user-specified dynamical processes in large-scale networks, representing (i) ion channels, (ii) synaptic receptors that may be subject to a-priori arbitrary plasticity processes, or (iii) slow processes describing molecular signaling or ion concentration dynamics. With the present work, we facilitate the creation and efficient, distributed simulations of such networks, thus supporting the investigation of the role of dendritic processes in network-level computations involving learning and memory. 21 References References [1] M.-O. Gewaltig and M. Diesmann, “NEST (NEural Simulation Tool),” Scholarpedia, vol. 2, no. 4, p. 1430, 2007, doi: 10.4249/scholarpedia.1430. [2] S. Kunkel et al., “Spiking network simulation code for petascale computers,” Front. Neuroinformatics, vol. 8, Oct. 2014, doi: 10.3389/fninf.2014.00078. [3] I. Blundell, D. Plotnikov, J. M. Eppler, and A. Morrison, “Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models,” Front. Neuroinformatics, vol. 12, p. 50, Oct. 2018, doi: 10.3389/fninf.2018.00050. [4] M. Hines, “Efficient computation of branched nerve equations,” Int. J. Biomed. Comput., vol. 15, no. 1, pp. 69–75, 1984. [5] N. T. Carnevale and M. L. Hines, The NEURON book. 2004. [6] M. E. Larkum, J. J. Zhu, and B. Sakmann, “A new cellular mechanism for coupling inputs arriving at different cortical layers.,” Nature, vol. 398, no. 6725, pp. 338– 41, Mar. 1999, doi: 10.1038/18686. [7] E. Pastorelli et al., “Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes,” Mar. 26, 2024, arXiv: arXiv:2311.06074. doi: 10.48550/arXiv.2311.06074. References (DOI) [1] 10.4249/scholarpedia.1430 [2] 10.3389/fninf.2014.00078 [3] 10.3389/fninf.2018.00050 [4] 10.1017/CBO9780511541612 [5] 10.1016/0020-7101(84)90008-4 [6] 10.1038/18686 [7] 10.48550/arXiv.2311.0607 Acknowledgements The authors gratefully acknowledge funding from the HelmHoltz POF IV, Program 2 Topic 3. 22 T-6 – Modeling Calcium-Mediated Spike-Timing Dependent Plasticity in Spiking Neural Networks Carlo Andrea Sartori,Politecnico di Milano Authors: Carlo Andrea Sartori, Francesco De Santis, Alberto Antonietti, Alessandra Pedrocchi, Leo Cottini, Matteo Maresca, Riccardo Francesco Mainetti Keywords: Synaptic Plasticity, Calcium Dynamics, STDP, Spiking Neural Network This study translates the model of Chindemi et al. on calciumdependent neocortical plasticity into a spiking neural network framework. Building on their work, we implemented a computationally efficient model comprising a point neuron and synapse model, using NESTML. Our approach combines the Hill-Tononi (HT) neuron, which features detailed NMDA and AMPA conductance dynamics, with the Tsodyks-Markram (TM) stochastic synapse, which controls vesicle release probability. We extended these components to create a comprehensive framework that captures the relationship between calcium dynamics and spike-timing dependent plasticity while maintaining computational efficiency for large-scale network simulations. Both our model and Chindemi’s rely on the assumption that calcium-dependent processes following paired preand post-synaptic activity influence synaptic efficacy on both sides of the synapse: by modifying the maximum AMPA conductance (GAMPA) at the post-synaptic site and the release probability (USE) at the synapse. We validated our implementation through a series of experiments: first confirming the functionality of the TM synapse model paired with HT neuron modifications to account for calcium currents, then testing isolated preand post-synaptic activations, generating NMDA and VDCC calcium currents respectively. Finally, we examined paired pre-post stimulation at varying time intervals. Our results successfully replicate Chindemi’s findings obtained with more complex multicompartmental models, also assessing plasticity outcomes according to the distance of the synaptic input to the soma, as experimental evidence shown by P. J. Sjöström and M. Haüsser. This work bridges neuronal activity patterns and synaptic modifications underlying learning and memory. References [1] R. S. Zucker, “Calciumand activity-dependent synaptic plasticity”, Current Opinion in Neurobiology, 1999. [2] G. Chindemi et al, “A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex”, Nature Communications, 2022. [3] M. Graupner and N. Brunel, “Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location”, PNAS, 2012. [4] S. Hill and G. Tononi, “Modeling Sleep and Wakefulness in the Thalamocortical System”, Journal Of Neurophysiology, 2005. [5] M.V. Tsodyks and H. Markram, “The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability”, PNAS, 1997. [6] P. J. Sjöström and M. Haüsser, “A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons”, Neuron, 2006. Acknowledgements The work of AA, AP, CAS, and FDS in this research is supported by Horizon Europe Program for Research and Innovation under Grant Agreement No.101147319 (EBRAINS 2.0) and EBRAINS-Italy (European Brain ReseArch INfrastructureSItaly), granted by the Italian National Recovery and Resilience Plan (NRRP), M4C2, funded by the EuropeanUnion – NextGenerationEU (Project IR0000011, CUP B51E22000150006, EBRAINS-Italy). 23 T-7 – Towards an empirically based model of the prefrontal cortex using AdEx neurons and NEST Pedro Ribeiro Pinheiro,Universidade de São Paulo Authors: Pedro Ribeiro Pinheiro, Antônio Roque Keywords: Prefrontal cortex, large-scale network model, AdEx neuron, cortical dynamics We present the development of a biologically grounded spiking neuronal network model of the prefrontal cortex (PFC), implemented in the NEST simulator using the adaptive exponential integrate-and-fire (AdEx) neuron model. Based on the architecture proposed by Hass et al. (2016), our model replaces the simplified AdEx (simpAdEx) neurons used in their study with the full AdEx model, thereby expanding the dynamical repertoire of individual neurons while preserving the original network topology. The network comprises 1,000 neurons distributed across two excitatory and eight inhibitory populations, spanning cortical layers 2/3 and 5. Implementation is carried out in PyNEST, with neuron and synapse models defined in NESTML. The model supports AMPA, NMDA, and GABAergic synapses, each described by double-exponential kinetics, and includes a 30% synaptic transmission failure rate. Synaptic weights and delays are sampled from log-normal and normal distributions, respectively. Connectivity is defined by population-specific connection probabilities, and synapses follow the Tsodyks-Markram (2002) shortterm plasticity dynamics. The goal is to provide a robust, data-driven, and openly accessible model of PFC circuitry to the NEST community. Preliminary simulations show that the network produces realistic membrane potential traces and supports asynchronous-irregular firing states consistent with in vivo cortical activity. This model offers a flexible platform for exploring prefrontal cortical dynamics and serves as a foundation for investigating both physiological and pathological states in largescale simulations. References Hass J, Hertäg L, Durstewitz D. A detailed data-driven network model of prefrontal cortex reproduces key features of in vivo activity. PLoS Comput. Biol. 12:e1004930, 2016. Maass W, and Markram H. Synapses as dynamic memory buffers. Neural Netw. 15:155–161, 2002. Acknowledgements This work is part of the activities of the São Paulo Research Foundation (FAPESP) Research, Innovation, and Dissemination Center for Neuromathematics (grant nº 2013/ 07699-0). PRP is supported by a FAPESP MSc scholarship (grant nº 2024/16557-9). ACR is partially supported by a Brazilian National Council for Scientific and Technological Development (CNPq) research grant nº 303359/2022-6. The opinions, hypotheses and conclusions or recommendations expressed in this material are the responsibility of the authors and do not necessarily reflect the views of FAPESP and CNPq. 24 Workshops W-1 – Using NESTML and NEAT to create massively parallel network simulations with dendritic subunits Leander Ewert,IAS-6 Forschungszentrum Jülich and Willem Wybo,Forschungszentrum Jülich Authors: Leander Ewert, Willem Wybo Keywords: compartmental models, dendrites The brain is a massively parallel computer. In the human brain, 86 billion neurons convert synaptic inputs into action potential (AP) output. Moreover, even at the subcellular level, computations proceed in a massively parallel fashion, e.g. in each dendritic compartment. It is only natural, thus, to use the parallelisation and vectorisation capabilities of modern supercomputers to simulate the brain in a massively parallel fashion. The NEural Simulation Tool (NEST) is the reference with regards to the massively parallel simulation of spiking network models. We have extended the scope of the NESTML modelling language to support vectorized multi-compartment models, with dendrites featuring userspecified dynamical processes. This allows users to define simple multi-compartmental neuron models through NEST’s PyNest API, capturing key dendritic computations in large network models. A new complementary tool in the NEST-Initiative, the NEuronal Analysis Toolkit (NEAT), then allows full biophysical models and simplifications thereof to be included in NEST network simulations. This tool provides high-level functionalities for defining biophysically realistic neuron models, and extensive toolchains to simplify these complex models. The resulting simplifications can be exported programmatically to NEST, and thus embedded in network simulations. In this workshop, we will go over the mechanics of defining and compiling compartmental models using NESTML, of implementing simple compartmental layouts through the PyNEST API, and of creating biophysical models in NEAT, and embedding their simplifications in NEST network simulations. We will present this in the form of an interactive Jupyter notebook tutorial, which will subsequently become part of NEST’s documentation. 25