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| 001 | 1041671 | ||
| 005 | 20250505202224.0 | ||
| 037 | _ | _ | |a FZJ-2025-02376 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Bouss, Peter |0 P:(DE-Juel1)178725 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a DPG Spring Meeting of the Condensed Matter Section |c Regensburg |d 2025-03-16 - 2025-03-21 |w Germany |
| 245 | _ | _ | |a Employing normalizing flows to examine neural manifold characteristics and curvatures |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1746441693_14470 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Despite the vast number of active neurons, neuronal population activity supposedly lies on low-dimensional manifolds (Gallego et al., 2017). To learn the statistics of neural activity, we use Normalizing Flows (NFs) (Dinh et al., 2014). These neural networks are trained to estimate the probability distribution by learning an invertible map to a latent distribution.We adjust NF’s training objectives to distinguish between relevant and noise dimensions, by using a nested dropout procedure in the latent space (Bekasov & Murray, 2020). An approximation of the network for each mixture component as a quadratic mapping enables us to calculate the Riemannian curvature tensors of the neural manifold. We focus mainly on the directions in the tangent space, in which the sectional curvature shows local extrema.Finally, we apply the method to electrophysiological recordings of the visual cortex in macaques (Chen et al., 2022). We show that manifolds deviate significantly from being flat. Analyzing the curvature of the manifolds yields insights into the regimes where neuron groups interact in a non-linear manner. |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 1 |
| 536 | _ | _ | |a GRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240) |0 G:(GEPRIS)368482240 |c 368482240 |x 2 |
| 536 | _ | _ | |a RenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A) |0 G:(DE-Juel-1)BMBF-01IS19077A |c BMBF-01IS19077A |x 3 |
| 700 | 1 | _ | |a Nestler, Sandra |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Fischer, Kirsten |0 P:(DE-Juel1)180150 |b 2 |u fzj |
| 700 | 1 | _ | |a Merger, Claudia Lioba |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Rene, Alexandre |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Helias, Moritz |0 P:(DE-Juel1)144806 |b 5 |e Last author |u fzj |
| 856 | 4 | _ | |u https://www.dpg-verhandlungen.de/year/2025/conference/regensburg/part/soe/session/7/contribution/9 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1041671 |p VDB |
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| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 0 |6 P:(DE-Juel1)178725 |
| 910 | 1 | _ | |a Technion, Haifa, Israel |0 I:(DE-HGF)0 |b 1 |6 P:(DE-HGF)0 |
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| 910 | 1 | _ | |a SISSA, Trieste, Italy |0 I:(DE-HGF)0 |b 3 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 4 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a University of Ottawa, Canada |0 I:(DE-HGF)0 |b 4 |6 P:(DE-HGF)0 |
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| 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 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)POF4-5234 |x 1 |
| 914 | 1 | _ | |y 2025 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
| 980 | _ | _ | |a conf |
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| 980 | _ | _ | |a UNRESTRICTED |
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