001043040 001__ 1043040
001043040 005__ 20250627204348.0
001043040 0247_ $$2arXiv$$aarXiv:2505.21185
001043040 0247_ $$2doi$$a10.48550/arXiv.2505.21185
001043040 037__ $$aFZJ-2025-02733
001043040 1001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b0$$eCorresponding author
001043040 245__ $$aConstructive community race: full-density spiking neural network model drives neuromorphic computing
001043040 260__ $$barXiv$$c2025
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001043040 520__ $$aThe local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features of brain activity but its impact remained limited because of its computational demands. For theory and simulation, however, the model was a breakthrough because it removes uncertainties of downscaling, and larger models are less densely connected. This sparked a race in the neuromorphic computing community and the model became a de facto standard benchmark. Within a few years real-time performance was reached and surpassed at significantly reduced energy consumption. We review how the computational challenge was tackled by different simulation technologies and derive guidelines for the next generation of benchmarks and other domains of science.
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001043040 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x3
001043040 536__ $$0G:(DE-Juel-1)HiRSE_PS-20220812$$aHiRSE_PS - Helmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812)$$cHiRSE_PS-20220812$$x4
001043040 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x5
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001043040 650_7 $$2Other$$aPerformance (cs.PF)
001043040 650_7 $$2Other$$aDistributed, Parallel, and Cluster Computing (cs.DC)
001043040 650_7 $$2Other$$aFOS: Computer and information sciences
001043040 7001_ $$aKurth, Anno C.$$b1
001043040 7001_ $$aFurber, Steve$$b2
001043040 7001_ $$0P:(DE-HGF)0$$aGemmeke, Tobias$$b3
001043040 7001_ $$aGolosio, Bruno$$b4
001043040 7001_ $$aHeittmann, Arne$$b5
001043040 7001_ $$aKnight, James C.$$b6
001043040 7001_ $$aMüller, Eric$$b7
001043040 7001_ $$aNoll, Tobias$$b8
001043040 7001_ $$aNowotny, Thomas$$b9
001043040 7001_ $$0P:(DE-HGF)0$$aCoppola, Gorka Peraza$$b10
001043040 7001_ $$aPeres, Luca$$b11
001043040 7001_ $$aRhodes, Oliver$$b12
001043040 7001_ $$aRowley, Andrew$$b13
001043040 7001_ $$aSchemmel, Johannes$$b14
001043040 7001_ $$0P:(DE-HGF)0$$aStadtmann, Tim$$b15
001043040 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b16$$ufzj
001043040 7001_ $$aTiddia, Gianmarco$$b17
001043040 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha J.$$b18$$ufzj
001043040 7001_ $$0P:(DE-Juel1)191583$$aVillamar, José$$b19$$ufzj
001043040 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b20$$ufzj
001043040 773__ $$a10.48550/arXiv.2505.21185$$tarXiv$$y2025
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001043040 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
001043040 9141_ $$y2025
001043040 920__ $$lno
001043040 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001043040 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
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