Models that contain the Model Concept : Attractor Neural Network

(A recurrently connected neural network with iterative activation update. A noisy initial pattern (of neural activity) will converge to one of a set of template patterns that is stored in the network by its weighted connections. Because of this property it is also a model of Associative Memory. The Continuous Attractor Network uses continuous activation values (~ firing rates), while the Binary Attractor Network uses {0,1} or {-1,1} as possible neuronal activations.)
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    Models
1. An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al., 2013)
2. Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)
3. Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
4. Feedforward heteroassociative network with HH dynamics (Lytton 1998)
5. Fixed point attractor (Hasselmo et al 1995)
6. Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007)
7. Grid cell spatial firing models (Zilli 2012)
8. Hierarchical network model of perceptual decision making (Wimmer et al 2015)
9. High dimensional dynamics and low dimensional readouts in neural microcircuits (Haeusler et al 2006)
10. Hopfield and Brody model (Hopfield, Brody 2000)
11. Hopfield and Brody model (Hopfield, Brody 2000) (NEURON+python)
12. Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)
13. MEC layer II stellate cell: Synaptic mechanisms of grid cells (Schmidt-Hieber & Hausser 2013)
14. Multistability of clustered states in a globally inhibitory network (Chandrasekaran et al. 2009)
15. Noise promotes independent control of gamma oscillations and grid firing (Solanka et al 2015)
16. Self-organized olfactory pattern recognition (Kaplan & Lansner 2014)
17. Stable propagation of synchronous spiking in cortical neural networks (Diesmann et al 1999)

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