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Köhler CA, Ulianych D, Grün S, Decker S, Denker M. Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture. eNeuro 2024; 11:ENEURO.0476-23.2024. [PMID: 38777610 PMCID: PMC11181106 DOI: 10.1523/eneuro.0476-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 02/28/2024] [Accepted: 04/13/2024] [Indexed: 05/25/2024] Open
Abstract
Scientific research demands reproducibility and transparency, particularly in data-intensive fields like electrophysiology. Electrophysiology data are typically analyzed using scripts that generate output files, including figures. Handling these results poses several challenges due to the complexity and iterative nature of the analysis process. These stem from the difficulty to discern the analysis steps, parameters, and data flow from the results, making knowledge transfer and findability challenging in collaborative settings. Provenance information tracks data lineage and processes applied to it, and provenance capture during the execution of an analysis script can address those challenges. We present Alpaca (Automated Lightweight Provenance Capture), a tool that captures fine-grained provenance information with minimal user intervention when running data analysis pipelines implemented in Python scripts. Alpaca records inputs, outputs, and function parameters and structures information according to the W3C PROV standard. We demonstrate the tool using a realistic use case involving multichannel local field potential recordings of a neurophysiological experiment, highlighting how the tool makes result details known in a standardized manner in order to address the challenges of the analysis process. Ultimately, using Alpaca will help to represent results according to the FAIR principles, which will improve research reproducibility and facilitate sharing the results of data analyses.
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Affiliation(s)
- Cristiano A Köhler
- Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
- Theoretical Systems Neurobiology, RWTH Aachen University, 52062 Aachen, Germany
| | - Danylo Ulianych
- Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
| | - Sonja Grün
- Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
- Theoretical Systems Neurobiology, RWTH Aachen University, 52062 Aachen, Germany
| | - Stefan Decker
- Chair of Databases and Information Systems, RWTH Aachen University, 52074 Aachen, Germany
- Fraunhofer Institute for Applied Information Technology (FIT), 53757 Sankt Augustin, Germany
| | - Michael Denker
- Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
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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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Tiddia G, Golosio B, Albers J, Senk J, Simula F, Pronold J, Fanti V, Pastorelli E, Paolucci PS, van Albada SJ. Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster. Front Neuroinform 2022; 16:883333. [PMID: 35859800 PMCID: PMC9289599 DOI: 10.3389/fninf.2022.883333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/02/2022] [Indexed: 11/29/2022] Open
Abstract
Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST.
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Affiliation(s)
- Gianmarco Tiddia
- Department of Physics, University of Cagliari, Monserrato, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, Italy
| | - Bruno Golosio
- Department of Physics, University of Cagliari, Monserrato, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, Italy
- *Correspondence: Bruno Golosio
| | - Jasper Albers
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Johanna Senk
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Francesco Simula
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Jari Pronold
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Viviana Fanti
- Department of Physics, University of Cagliari, Monserrato, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, Italy
| | - Elena Pastorelli
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | | | - Sacha J. van Albada
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Faculty of Mathematics and Natural Sciences, Institute of Zoology, University of Cologne, Cologne, Germany
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