Models that contain the Cell : Abstract integrate-and-fire leaky neuron

(A simple electrical model of a neuron first introduced in (Lapicque 1907))
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    Models
1. A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)
2. A spiking NN for amplification of feature-selectivity with specific connectivity (Sadeh et al 2015)
3. Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)
4. Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007)
5. Dentate Gyrus model including Granule cells with dendritic compartments (Chavlis et al 2017)
6. Diffusive homeostasis in a spiking network model (Sweeney et al. 2015)
7. Double boundary value problem (A. Bose and J.E. Rubin, 2015)
8. Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)
9. Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)
10. Generalized Carnevale-Hines algorithm (van Elburg and van Ooyen 2009)
11. Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)
12. Grid cell spatial firing models (Zilli 2012)
13. Hierarchical network model of perceptual decision making (Wimmer et al 2015)
14. Hippocampal spiking model for context dependent behavior (Raudies & Hasselmo 2014)
15. I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)
16. Inhibitory cells enable sparse coding in V1 model (King et al. 2013)
17. Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006)
18. Modelling the effects of short and random proto-neural elongations (de Wiljes et al 2017)
19. Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017)
20. Multi-timescale adaptive threshold model (Kobayashi et al 2009)
21. Multi-timescale adaptive threshold model (Kobayashi et al 2009) (NEURON)
22. Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
23. Neural transformations on spike timing information (Tripp and Eliasmith 2007)
24. Neuronify: An Educational Simulator for Neural Circuits (Dragly et al 2017)
25. Norns - Neural Network Studio (Visser & Van Gils 2014)
26. Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
27. Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
28. Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
29. Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007)
30. Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
31. Population models of temporal differentiation (Tripp and Eliasmith 2010)
32. Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
33. Spiking neuron model of the basal ganglia (Humphries et al 2006)
34. STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
35. Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
36. Supervised learning in spiking neural networks with FORCE training (Nicola & Clopath 2017, accepted)
37. Theory and simulation of integrate-and-fire neurons driven by shot noise (Droste & Lindner 2017)

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