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Schmitt FJ, Rostami V, Nawrot MP. Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST. Front Neuroinform 2023; 17:941696. [PMID: 36844916 PMCID: PMC9950635 DOI: 10.3389/fninf.2023.941696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 01/16/2023] [Indexed: 02/12/2023] Open
Abstract
Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106 neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.
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Affiliation(s)
- Felix Johannes Schmitt
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany
| | - Vahid Rostami
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany
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Masoli S, Rizza MF, Tognolina M, Prestori F, D’Angelo E. Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation. Front Comput Neurosci 2022; 16:1006989. [PMID: 36387305 PMCID: PMC9649760 DOI: 10.3389/fncom.2022.1006989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | | | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- *Correspondence: Francesca Prestori,
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Brain Connectivity Center, Pavia, Italy
- Egidio D’Angelo,
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Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System. Bioengineering (Basel) 2022; 9:bioengineering9100543. [PMID: 36290510 PMCID: PMC9598639 DOI: 10.3390/bioengineering9100543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/20/2022] [Accepted: 10/07/2022] [Indexed: 12/03/2022] Open
Abstract
The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin–Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.
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Kiwitz K, Brandstetter A, Schiffer C, Bludau S, Mohlberg H, Omidyeganeh M, Massicotte P, Amunts K. Cytoarchitectonic Maps of the Human Metathalamus in 3D Space. Front Neuroanat 2022; 16:837485. [PMID: 35350721 PMCID: PMC8957853 DOI: 10.3389/fnana.2022.837485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
The human metathalamus plays an important role in processing visual and auditory information. Understanding its layers and subdivisions is important to gain insights in its function as a subcortical relay station and involvement in various pathologies. Yet, detailed histological references of the microanatomy in 3D space are still missing. We therefore aim at providing cytoarchitectonic maps of the medial geniculate body (MGB) and its subdivisions in the BigBrain – a high-resolution 3D-reconstructed histological model of the human brain, as well as probabilistic cytoarchitectonic maps of the MGB and lateral geniculate body (LGB). Therefore, histological sections of ten postmortem brains were studied. Three MGB subdivisions (MGBv, MGBd, MGBm) were identified on every 5th BigBrain section, and a deep-learning based tool was applied to map them on every remaining section. The maps were 3D-reconstructed to show the shape and extent of the MGB and its subdivisions with cellular precision. The LGB and MGB were additionally identified in nine other postmortem brains. Probabilistic cytoarchitectonic maps in the MNI “Colin27” and MNI ICBM152 reference spaces were computed which reveal an overall low interindividual variability in topography and extent. The probabilistic maps were included into the Julich-Brain atlas, and are freely available. They can be linked to other 3D data of human brain organization and serve as an anatomical reference for diagnostic, prognostic and therapeutic neuroimaging studies of healthy brains and patients. Furthermore, the high-resolution MGB BigBrain maps provide a basis for data integration, brain modeling and simulation to bridge the larger scale involvement of thalamocortical and local subcortical circuits.
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Affiliation(s)
- Kai Kiwitz
- Cécile and Oskar Vogt Institute of Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstraße 1a, Leipzig, Germany
- *Correspondence: Kai Kiwitz,
| | - Andrea Brandstetter
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Christian Schiffer
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
- Helmholtz AI, Forschungszentrum Jülich, Jülich, Germany
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Mona Omidyeganeh
- McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- National Research Council of Canada, Ottawa, ON, Canada
| | - Philippe Massicotte
- McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Katrin Amunts
- Cécile and Oskar Vogt Institute of Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstraße 1a, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
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