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Statistical learning in biological neural networks
Multi angle
Authors : Schmidt-Hieber, Johannes (Author of the conference)
CIRM (Publisher )
62J05 - Linear regression
62L20 - Stochastic approximation
CIRM (Publisher )
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Abstract :
Compared to artificial neural networks (ANNs), the brain seems to learn faster, generalize better to new situations and consumes much less energy. ANNs are motivated by the functioning of the brain but differ in several crucial aspects. While ANNs are deterministic, biological neural networks (BNNs) are stochastic. Moreover, it is biologically implausible that the learning of the brain is based on gradient descent. In the past years, statistical theory for artificial neural networks has been developed. The idea now is to extend this to biological neural networks, as the future of AI is likely to draw even more inspiration from biology. In this lecture series we will survey the challenges and present some first statistical risk bounds for different biologically inspired learning rules.
Keywords : biological learning; neural networks; statistical estimation rates; supervised learning; zeroth-order methods
MSC Codes : Keywords : biological learning; neural networks; statistical estimation rates; supervised learning; zeroth-order methods
62J05 - Linear regression
62L20 - Stochastic approximation
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Information on the Video
Film maker : Recanzone, LucaLanguage : English Available date : 14/01/2025 Conference Date : 17/12/2024 Subseries : Research School arXiv category : Machine Learning ; Statistics Theory Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 00:57:30 Targeted Audience : Researchers ; Graduate Students ; Doctoral Students, Post-Doctoral Students Download : https://videos.cirm-math.fr/2024-12-17_Schmidt-Hieber.mp4 
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Information on the Event
Event Title : New challenges in high-dimensional statistics / Statistique mathématiqueEvent Organizers : Klopp, Olga ; Pouet, Christophe ; Rakhlin, Alexander Dates : 16/12/2024 - 20/12/2024 Event Year : 2024 Event URL : https://conferences.cirm-math.fr/3055.html
Citation Data
DOI : 10.24350/CIRM.V.20279603Cite this video as: Schmidt-Hieber, Johannes (2024). Statistical learning in biological neural networks. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.20279603 URI : http://dx.doi.org/10.24350/CIRM.V.20279603 |
See Also
- [Multi angle] Are adaptive robust confidence intervals possible? / Author of the conference Gao, Chao.
- [Multi angle] Attention layers provably solve single-location regression / Author of the conference Boyer, Claire.
- [Multi angle] An introduction to conformal prediction and distribution-free inference / Author of the conference Barber, Rina Foygel.
- [Multi angle] From robust tests to robust Bayes-like posterior distribution / Author of the conference Baraud, Yannick.
Bibliography
- SCHMIDT-HIEBER, Johannes. Interpreting learning in biological neural networks as zero-order optimization method. arXiv preprint arXiv:2301.11777, 2023. - https://doi.org/10.48550/arXiv.2301.11777
- BOS, Thijs et SCHMIDT-HIEBER, Johannes. Convergence guarantees for forward gradient descent in the linear regression model. Journal of Statistical Planning and Inference, 2024, vol. 233, p. 106174. - https://doi.org/10.1016/j.jspi.2024.106174
- SCHMIDT-HIEBER, Johannes et KOOLEN, Wouter M. Hebbian learning inspired estimation of the linear regression parameters from queries. arXiv preprint arXiv:2311.03483, 2023. - https://doi.org/10.48550/arXiv.2311.03483
- DEXHEIMER, Niklas et SCHMIDT-HIEBER, Johannes. Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling. arXiv preprint arXiv:2411.17567, 2024. - https://doi.org/10.48550/arXiv.2411.17567
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