Models that contain the Model Concept : Unsupervised Learning

(A method for neural network training where the network is only presented with inputs and tries to find patterns within the inputs to classify the inputs, or alternatively, attempts to maximize a fitness function by exploring an environment without any best output pattern made available.)
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    Models
1. 3D model of the olfactory bulb (Migliore et al. 2014)
2. 3D olfactory bulb: operators (Migliore et al, 2015)
3. Alternative time representation in dopamine models (Rivest et al. 2009)
4. Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
5. Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
6. Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
7. Large scale model of the olfactory bulb (Yu et al., 2013)
8. Learning spatial transformations through STDP (Davison, Frégnac 2006)
9. Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)
10. Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
11. Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
12. Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
13. Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)
14. Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
15. STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)

Re-display model names with descriptions