Models that contain the Modeling Application : NEST ()

("NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. The development of NEST is coordinated by the NEST Initiative. NEST is ideal for networks of spiking neurons of any size, for example: 1. Models of information processing e.g. in the visual or auditory cortex of mammals, 2. Models of network activity dynamics, e.g. laminar cortical networks or balanced random networks, 3. Models of learning and plasticity." ) Re-display model names with descriptions

	Models
1.	A spatial model of the intermediate superior colliculus (Moren et. al. 2013)
2.	A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)
3.	A spiking NN for amplification of feature-selectivity with specific connectivity (Sadeh et al 2015)
4.	An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
5.	Brain networks simulators - a comparative study (Tikidji-Hamburyan et al 2017)
6.	Complex dynamics: reproducing Golgi cell electroresponsiveness (Geminiani et al 2018, 2019ab)
7.	Cortical feedback alters visual response properties of dLGN relay cells (Martínez-Cañada et al 2018)
8.	eLIF and mAdExp: energy-based integrate-and-fire neurons (Fardet and Levina 2020)
9.	Emergence of spatiotemporal sequences in spiking neuronal networks (Spreizer et al 2019)
10.	GLMCC validation neural network model (Kobayashi et al. 2019)
11.	Growth Rules for Repair of Asynch Irregular Networks after Peripheral Lesions (Sinha et al 2021)
12.	Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex
13.	Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
14.	Neuromorphic muscle spindle model (Vannucci et al 2017)
15.	On the structural connectivity of large-scale models of brain networks (Giacopelli et al 2021)
16.	Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
17.	Sparsely connected networks of spiking neurons (Brunel 2000)
18.	Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)

Re-display model names with descriptions