001044915 001__ 1044915
001044915 005__ 20251024202103.0
001044915 037__ $$aFZJ-2025-03437
001044915 1001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b0$$eCorresponding author$$ufzj
001044915 1112_ $$aFIAS Neuroscience Seminar$$cFrankfurt$$d2024-10-15 - 2024-10-16$$wGermany
001044915 245__ $$aSpike patterns in nature and AI$$f2024-10-16 - 
001044915 260__ $$c2024
001044915 3367_ $$033$$2EndNote$$aConference Paper
001044915 3367_ $$2DataCite$$aOther
001044915 3367_ $$2BibTeX$$aINPROCEEDINGS
001044915 3367_ $$2ORCID$$aLECTURE_SPEECH
001044915 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1761311861_7868$$xInvited
001044915 3367_ $$2DINI$$aOther
001044915 520__ $$aThe energy consumption of present AI systems is unsustainable and undemocratic. Understanding the energy efficiency of the brain may uncover pathways out of the dilemma. A promising observation is that unlike artificial neural networks, mammalian brains are sparse in space and time. Spatial sparseness means that neurons are only connected to a tiny fraction of the other neurons, temporal sparseness means that on the few connections there is activity only a few times per second. This architecture suggests that the brain relies on the coordinated activity of populations of neurons to process sensory information and organize behavior.In the past three decades computational neuroscience has learned how to extract and interpret the dynamics of spatio-temporal patterns in massively parallel spike trains. Despite this progress it is still unclear how function and in particular learning is implemented by the mammalian cortex. Reasons may be the undersampling of the system and the limited capability to observe the system during learning.Ironically in artificial neural networks we have a similar situation. These networks can learn complex tasks with high accuracy. But our understanding of what has been learned and how the trained network solves the task is limited. Progress has already been made by transferring ideas of generic learning algorithms for artificial neural networks to spiking systems. This gives us benchmarks on what we can find out about such a system with statistical tools, but the analysis may also give inspiration on the working principles of the natural system.The talk first reviews the state-of-the-art of the time-resolved analysis of spike patterns required for undersampled brain data. Subsequently, the talk reports on the analysis of a spike-based artificial neural network trained on the MNIST data set, which exhibits precise spike timing.
001044915 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001044915 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001044915 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x2
001044915 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x3
001044915 536__ $$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$$x4
001044915 536__ $$0G:(DE-Juel-1)iBehave-20220812$$aAlgorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812)$$ciBehave-20220812$$x5
001044915 536__ $$0G:(BMBF)16ME0400$$aBMBF 16ME0400 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (16ME0400)$$c16ME0400$$x6
001044915 536__ $$0G:(EU-Grant)956669$$aIn2PrimateBrains - Intra- and Inter-Areal Communication in Primate Brain Networks (956669)$$c956669$$fH2020-MSCA-ITN-2020$$x7
001044915 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x8
001044915 909CO $$ooai:juser.fz-juelich.de:1044915$$popenaire$$pVDB$$pec_fundedresources
001044915 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b0$$kFZJ
001044915 9131_ $$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
001044915 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$$x1
001044915 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001044915 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
001044915 980__ $$atalk
001044915 980__ $$aVDB
001044915 980__ $$aI:(DE-Juel1)IAS-6-20130828
001044915 980__ $$aI:(DE-Juel1)INM-10-20170113
001044915 980__ $$aUNRESTRICTED