001045452 001__ 1045452
001045452 005__ 20251015202130.0
001045452 0247_ $$2doi$$a10.48550/ARXIV.2508.12975
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001045452 1001_ $$0P:(DE-Juel1)186076$$aOberste-Frielinghaus, Jonas$$b0$$eCorresponding author$$ufzj
001045452 245__ $$aSynchronization and semantization in deep spiking networks
001045452 260__ $$barXiv$$c2025
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001045452 520__ $$aRecent studies have shown how spiking networks can learn complex functionality through error-correcting plasticity, but the resulting structures and dynamics remain poorly studied. To elucidate how these models may link to observed dynamics in vivo and thus how they may ultimately explain cortical computation, we need a better understanding of their emerging patterns. We train a multi-layer spiking network, as a conceptual analog of the bottom-up visual hierarchy, for visual input classification using spike-time encoding. After learning, we observe the development of distinct spatio-temporal activity patterns. While input patterns are synchronous by construction, activity in early layers first spreads out over time, followed by re-convergence into sharp pulses as classes are gradually extracted. The emergence of synchronicity is accompanied by the formation of increasingly distinct pathways, reflecting the gradual semantization of input activity. We thus observe hierarchical networks learning spike latency codes to naturally acquire activity patterns characterized by synchronicity and separability, with pronounced excitatory pathways ascending through the layers. This provides a rigorous computational hypothesis for the experimentally observed synchronicity in the visual system as a natural consequence of deep learning in cortex.
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001045452 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
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001045452 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
001045452 536__ $$0G:(DE-Juel-1)iBehave-20220812$$aAlgorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812)$$ciBehave-20220812$$x4
001045452 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x5
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001045452 650_7 $$2Other$$aNeurons and Cognition (q-bio.NC)
001045452 650_7 $$2Other$$aNeural and Evolutionary Computing (cs.NE)
001045452 650_7 $$2Other$$aComputation (stat.CO)
001045452 650_7 $$2Other$$aFOS: Biological sciences
001045452 650_7 $$2Other$$aFOS: Computer and information sciences
001045452 7001_ $$0P:(DE-Juel1)176776$$aKurth, Anno C.$$b1$$ufzj
001045452 7001_ $$0P:(DE-HGF)0$$aGöltz, Julian$$b2
001045452 7001_ $$0P:(DE-HGF)0$$aKriener, Laura$$b3
001045452 7001_ $$0P:(DE-Juel1)144576$$aIto, Junji$$b4$$ufzj
001045452 7001_ $$0P:(DE-HGF)0$$aPetrovici, Mihai A.$$b5
001045452 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b6$$ufzj
001045452 773__ $$a10.48550/ARXIV.2508.12975
001045452 8564_ $$uhttps://doi.org/10.48550/arXiv.2508.12975
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001045452 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001045452 9141_ $$y2025
001045452 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001045452 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
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