Development of feature detectors by self-organization
By J. Rubner, Klaus Schulten. 2004
70 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Computer Networks and Communications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Anirvan M. Sengupta
- Dmitri "Mitya" Chklovskii
- Cengiz Pehlevan
- Tao Hu
- J. Weng
- Lars Hiller Eidnes
- Arild Nøkland
- Pierre-Yves Oudeyer
In Cognitive Neuroscience
- Anirvan M. Sengupta
- Dmitri "Mitya" Chklovskii
- Cengiz Pehlevan
- John A. Bullinaria
- Arild Nøkland
- Lars Hiller Eidnes
In Computer Networks and Communications
Citations in the corpus
- Deep learning in neural networks: An overview (2014, neutral): “…1990; Martinetz, Ritter, & Schulten, 1990; Miller, 1994; Mozer, 1991; Oja, 1989; Palm, 1992; Pearlmutter &Hinton, 1986; Ritter & Kohonen, 1989; Rubner & Schulten, 1990; Sanger, 1989; Saund, 1994; von der Malsburg, 1973; Watanabe, 1985; Willshaw & von der Malsburg, 1976); see also post-2000…”
- A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data (2015, background): “Some well-known examples are the Generalized Hebbian Algorithm (GHA) (Sanger, 1989), F ¨ oldiak’s network (F ¨ oldiak, 1989), the subspace network (Karhunen and Oja, 1982), Rubner’s network (Rubner and Tavan, 1989; Rubner and Schulten, 1990), Leen’s minimal coupling and full coupling networks (Leen,”
- Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks? (2017, methodology): “…to the self-organization of neural networks for solving unsupervised computational tasks using Hebbian and anti-Hebbian learning rules (F¨oldiak, 1990; F¨oldiak, 1989; Rubner and Tavan, 1989; Rubner and Schulten, 1990; Carlson, 1990; Plumbley, 1993b; Leen, 1991; Plumbley, 1993a; Linsker, 1997).”
- Candid Covariance-Free Incremental Principal Component Analysis (2003, background): “A clear answer is not available yet, but Rubner and Schulten [21] proved that the well-known mechanisms of biological Hebbian learning and lateral inhibition between nearby neurons [22, pp. 1,020 and 376] result in an incremental way of computing PCA.”