001052972 001__ 1052972 001052972 005__ 20260128204149.0 001052972 0247_ $$2doi$$a10.5281/ZENODO.17395886 001052972 037__ $$aFZJ-2026-01322 001052972 041__ $$aEnglish 001052972 1001_ $$0P:(DE-Juel1)180150$$aFischer, Kirsten$$b0$$eCorresponding author 001052972 245__ $$aField theory for optimal signal propagation in ResNets 001052972 260__ $$c2025 001052972 3367_ $$2DCMI$$aSoftware 001052972 3367_ $$0PUB:(DE-HGF)33$$2PUB:(DE-HGF)$$aSoftware$$bsware$$msware$$s1769603305_4882 001052972 3367_ $$2BibTeX$$aMISC 001052972 3367_ $$06$$2EndNote$$aComputer Program 001052972 3367_ $$2ORCID$$aOTHER 001052972 3367_ $$2DataCite$$aSoftware 001052972 520__ $$aThis repository contains the code accompanying the paper: Fischer, K., Dahmen, D., Helias, M. (2023). Field theory for optimal signal propagation in ResNets (arXiv:2305.07715). For any questions, please contact Kirsten Fischer (ki.fischer@fz-juelich.de). 001052972 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0 001052972 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1 001052972 536__ $$0G:(DE-Juel-1)BMBF-01IS19077A$$aRenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A)$$cBMBF-01IS19077A$$x2 001052972 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x3 001052972 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x4 001052972 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x5 001052972 536__ $$0G:(GEPRIS)491111487$$aDFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2025 - 2027 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x6 001052972 588__ $$aDataset connected to DataCite 001052972 650_7 $$2Other$$aField theory 001052972 650_7 $$2Other$$aResidual networks 001052972 650_7 $$2Other$$aMachine Learning 001052972 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b1 001052972 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2 001052972 773__ $$a10.5281/ZENODO.17395886 001052972 909CO $$ooai:juser.fz-juelich.de:1052972$$pVDB 001052972 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180150$$aForschungszentrum Jülich$$b0$$kFZJ 001052972 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156459$$aForschungszentrum Jülich$$b1$$kFZJ 001052972 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b2$$kFZJ 001052972 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$$x0 001052972 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1 001052972 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0 001052972 980__ $$asware 001052972 980__ $$aVDB 001052972 980__ $$aI:(DE-Juel1)IAS-6-20130828 001052972 980__ $$aUNRESTRICTED