Keywords:
- Afferent
- Afferent: classification
- Afferent: cytology
- Afferent: physiology
- Algorithms
- Animals
- Artificial Intelligence
- Automated
- complex cells
- Computer Simulation
- Computer-Assisted
- Data Interpretation
- Head Movements
- Head Movements: physiology
- Hippocampus
- Hippocampus: physiology
- Humans
- invariances
- invariant representations
- Linear Models
- Models
- Nerve Net
- Nerve Net: cytology
- Nerve Net: physiology
- Neural Networks (Computer)
- Neurological
- Neurons
- Nonlinear Dynamics
- Object Recognition
- orientation
- Orientation: physiology
- Pattern Recognition
- Photoreceptor Cells
- pose estimation
- Principal Component Analysis
- Rats
- receptive fields
- Signal Processing
- Slow Feature Analysis
- Space Perception
- Space Perception: physiology
- Statistical
- statistically independent sources
- theoretical analysis
- Time Factors
- unsupervised learning
- Visual Perception
- Visual Perception: physiology
- visual system
Publications of Laurenz Wiskott
2011
| , and , Invariant object recognition and pose estimation with slow feature analysis. (2011), in: Neural computation, 23:9(2289--323) |
[DOI] [URL] |
2008
| and , Understanding slow feature analysis: A mathematical framework (2008), in: Optimization |
[URL] |
2007
| , and , Slowness and sparseness lead to place, head-direction, and spatial-view cells. (2007), in: PLoS computational biology, 3:8(e166) |
[DOI] [URL] |
2006
| , and , What is the relation between slow feature analysis and independent component analysis? (2006), in: Neural computation, 18:10(2495--508) |
[DOI] [URL] |
2003
| and , Is slowness a learning principle of the visual cortex? (2003), in: Zoology (Jena, Germany), 106:4(373--82) |
[DOI] [URL] |
2002
| and , Slow feature analysis: unsupervised learning of invariances. (2002), in: Neural computation, 14:4(715--70) |
[DOI] [URL] |