Cargando…
Biological learning curves outperform existing ones in artificial intelligence algorithms
Recently, deep learning algorithms have outperformed human experts in various tasks across several domains; however, their characteristics are distant from current knowledge of neuroscience. The simulation results of biological learning algorithms presented herein outperform state-of-the-art optimal...
Autores principales: | Uzan, Herut, Sardi, Shira, Goldental, Amir, Vardi, Roni, Kanter, Ido |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688986/ https://www.ncbi.nlm.nih.gov/pubmed/31399614 http://dx.doi.org/10.1038/s41598-019-48016-4 |
Ejemplares similares
-
Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks
por: Uzan, Herut, et al.
Publicado: (2018) -
Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links
por: Sardi, Shira, et al.
Publicado: (2018) -
Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms
por: Sardi, Shira, et al.
Publicado: (2020) -
Publisher Correction: Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms
por: Sardi, Shira, et al.
Publicado: (2020) -
Oscillations in networks of networks stem from adaptive nodes with memory
por: Goldental, Amir, et al.
Publicado: (2017)