ModelDB: Models that contain Unsupervised Learning

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.

3D model of the olfactory bulb (Migliore et al. 2014)
3D olfactory bulb: operators (Migliore et al, 2015)
Alternative time representation in dopamine models (Rivest et al. 2009)
Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)
Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)
Large scale model of the olfactory bulb (Yu et al., 2013)
Learning spatial transformations through STDP (Davison, Frégnac 2006)
Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)
Optimal Localist and Distributed Coding Through STDP (Masquelier & Kheradpisheh 2018)
Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)
Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)

Top authors for Unsupervised Learning:

Masquelier T (5)
Migliore M (3)
Hines ML (3)
Shepherd GM (3)
Weber C (3)
Cavarretta F (2)
Triesch J (2)
Wermter S (2)
Hugues E (2)
Marasco A (1)

Top concepts studied with Unsupervised Learning:

Synaptic Plasticity (10)
Pattern Recognition (7)
Activity Patterns (7)
Long-term Synaptic Plasticity (7)
Learning (6)
STDP (6)
Temporal Pattern Generation (5)
Olfaction (5)
Coincidence Detection (5)
Spatio-temporal Activity Patterns (5)

Top neurons studied with Unsupervised Learning:

Abstract integrate-and-fire leaky neuron (5)
Olfactory bulb main mitral GLU cell (3)
Olfactory bulb main interneuron granule MC GABA cell (3)
Abstract rate-based neuron (2)
ELL pyramidal cell (1)
Thalamus geniculate nucleus/lateral principal GLU cell (1)
Neocortex L2/3 pyramidal GLU cell (1)
Retina ganglion GLU cell (1)
Retina photoreceptor cone GLU cell (1)
Retina bipolar GLU cell (1)

Top currents studied with Unsupervised Learning:

I Na,t (3)
I A (3)
I K (3)
I Cl, leak (1)

Top references cited by these models:

Song S, Miller KD, Abbott LF. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature neuroscience. 3 (6)
van Rossum MC, Bi GQ, Turrigiano GG. (2000). Stable Hebbian learning from spike timing-dependent plasticity. The Journal of neuroscience : the official journal of the Society for Neuroscience. 20 (6)
Gütig R, Aharonov R, Rotter S, Sompolinsky H. (2003). Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. The Journal of neuroscience : the official journal of the Society for Neuroscience. 23 (5)
Masquelier T, Guyonneau R, Thorpe SJ. (2009). Competitive STDP-based spike pattern learning. Neural computation. 21 (5)
Masquelier T, Guyonneau R, Thorpe SJ. (2008). Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PloS one. 3 (5)