Publication: Recurrent competitive networks can learn locally excitatory topologies
Recurrent competitive networks can learn locally excitatory topologies
Date
Date
Date
Citations
Jug, F., Cook, M., & Steger, A. (2012). Recurrent competitive networks can learn locally excitatory topologies. Proceedings of the International Joint Conference on Neural Networks, 1–8. https://doi.org/10.1109/IJCNN.2012.6252786
Abstract
Abstract
Abstract
A common form of neural network consists of spatially arranged neurons, with weighted connections between the units providing both local excitation and long-range or global inhibition. Such networks, known as soft-winner-take-all networks or lateral-inhibition type neural fields, have been shown to exhibit desirable information-processing properties including balancing the influence of compatible inputs, deciding between incompatible inputs, signal restoration from noisy, weak, or overly strong input, and the ability to be used as tra
Metrics
Additional indexing
Creators (Authors)
- Jug, Florian
- Cook, Matthew
- Steger, Angelika
Event Title
Event Title
Event Title
Event Location
Event Location
Event Location
Event Country
Event Country
Event Country
Event Start Date
Event Start Date
Event Start Date
Event End Date
Event End Date
Event End Date
Publisher
Publisher
Publisher
Page range/Item number
Page range/Item number
Page range/Item number
Page end
Page end
Page end
Item Type
Item Type
Item Type
In collections
Language
Language
Language
Date available
Date available
Date available
Series Name
Series Name
Series Name
ISSN or e-ISSN
ISSN or e-ISSN
ISSN or e-ISSN
ISBN or e-ISBN
ISBN or e-ISBN
ISBN or e-ISBN
Additional Information
Additional Information
Additional Information
OA Status
OA Status
OA Status
Publisher DOI
Metrics
Citations
Jug, F., Cook, M., & Steger, A. (2012). Recurrent competitive networks can learn locally excitatory topologies. Proceedings of the International Joint Conference on Neural Networks, 1–8. https://doi.org/10.1109/IJCNN.2012.6252786
