Gast ::
| Hauptseite > Publikationsdatenbank > Spike patterns in nature and AI > print |
| 001 | 1044915 | ||
| 005 | 20251024202103.0 | ||
| 037 | _ | _ | |a FZJ-2025-03437 |
| 100 | 1 | _ | |a Grün, Sonja |0 P:(DE-Juel1)144168 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a FIAS Neuroscience Seminar |c Frankfurt |d 2024-10-15 - 2024-10-16 |w Germany |
| 245 | _ | _ | |a Spike patterns in nature and AI |f 2024-10-16 - |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Talk (non-conference) |b talk |m talk |0 PUB:(DE-HGF)31 |s 1761311861_7868 |2 PUB:(DE-HGF) |x Invited |
| 336 | 7 | _ | |a Other |2 DINI |
| 520 | _ | _ | |a The 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. |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 1 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 2 |
| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 3 |
| 536 | _ | _ | |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) |0 G:(DE-Juel1)HDS-LEE-20190612 |c HDS-LEE-20190612 |x 4 |
| 536 | _ | _ | |a Algorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812) |0 G:(DE-Juel-1)iBehave-20220812 |c iBehave-20220812 |x 5 |
| 536 | _ | _ | |a BMBF 16ME0400 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (16ME0400) |0 G:(BMBF)16ME0400 |c 16ME0400 |x 6 |
| 536 | _ | _ | |a In2PrimateBrains - Intra- and Inter-Areal Communication in Primate Brain Networks (956669) |0 G:(EU-Grant)956669 |c 956669 |f H2020-MSCA-ITN-2020 |x 7 |
| 536 | _ | _ | |a GRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240) |0 G:(GEPRIS)368482240 |c 368482240 |x 8 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1044915 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)144168 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5231 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5232 |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 1 |
| 980 | _ | _ | |a talk |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|