Circuits 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.) Re-display model names with descriptions

	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.	Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)
6.	Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
7.	Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
8.	Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)
9.	Large scale model of the olfactory bulb (Yu et al., 2013)
10.	Learning spatial transformations through STDP (Davison, Frégnac 2006)
11.	Optimal Localist and Distributed Coding Through STDP (Masquelier & Kheradpisheh 2018)
12.	Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
13.	Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
14.	Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)
15.	Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
16.	STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)

Re-display model names with descriptions