Models that contain the Modeling Application : Brian (Home Page)

(Brian is a new simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists. Brian is easy to learn and use, highly flexible and easily extensible. The Brian package itself and simulations using it are all written in the Python programming language, which is an easy, concise and highly developed language with many advanced features and development tools, excellent documentation and a large community of users providing support and extension packages.)
Re-display model names with descriptions
    Models
1. A threshold equation for action potential initiation (Platkiewicz & Brette 2010)
2. Adaptive exponential integrate-and-fire model (Brette & Gerstner 2005)
3. An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al., 2013)
4. Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)
5. Brain networks simulators - a comparative study (Tikidji-Hamburyan et al 2017)
6. Brette-Gerstner model (Touboul and Brette 2008)
7. CA1 PV+ fast-firing hippocampal interneuron (Ferguson et al. 2013)
8. CA1 pyramidal neuron (Ferguson et al. 2014)
9. CA1 pyramidal neuron network model (Ferguson et al 2015)
10. CA1 SOM+ (OLM) hippocampal interneuron (Ferguson et al. 2015)
11. CN bushy, stellate neurons (Rothman, Manis 2003) (Brian 2)
12. CN bushy, stellate neurons (Rothman, Manis 2003) (Brian)
13. Computing with neural synchrony (Brette 2012)
14. CRH modulates excitatory transmission and network physiology in hippocampus (Gunn et al. 2017)
15. Dentate Gyrus model including Granule cells with dendritic compartments (Chavlis et al 2017)
16. Diffusive homeostasis in a spiking network model (Sweeney et al. 2015)
17. Effect of polysynaptic facilitaiton between piriform-hippocampal network stages (Trieu et al 2015)
18. Fast global oscillations in networks of I&F neurons with low firing rates (Brunel and Hakim 1999)
19. Gamma-beta alternation in the olfactory bulb (David, Fourcaud-Trocmé et al., 2015)
20. Hierarchical network model of perceptual decision making (Wimmer et al 2015)
21. High entrainment constrains synaptic depression in a globular bushy cell (Rudnicki & Hemmert 2017)
22. Impact of fast Na channel inact. on AP threshold & synaptic integration (Platkiewicz & Brette 2011)
23. In vivo imaging of dentate gyrus mossy cells in behaving mice (Danielson et al 2017)
24. Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011)
25. Input strength and time-varying oscillation peak frequency (Cohen MX 2014)
26. Late emergence of the whisker direction selectivity map in rat barrel cortex (Kremer et al. 2011)
27. Memory savings through unified pre- and postsynaptic STDP (Costa et al 2015)
28. Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)
29. Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
30. Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014)
31. Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
32. Phase locking in leaky integrate-and-fire model (Brette 2004)
33. Phase response curves firing rate dependency of rat purkinje neurons in vitro (Couto et al 2015)
34. Reliability of spike timing is a general property of spiking model neurons (Brette & Guigon 2003)
35. Sensitivity of noisy neurons to coincident inputs (Rossant et al. 2011)
36. Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)
37. Spontaneous weakly correlated excitation and inhibition (Tan et al. 2013)
38. Stable propagation of synchronous spiking in cortical neural networks (Diesmann et al 1999)
39. STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
40. STDP and oscillations produce phase-locking (Muller et al. 2011)
41. Theory of arachnid prey localization (Sturzl et al. 2000)
42. Time-warp-invariant neuronal processing (Gutig & Sompolinsky 2009)
43. Vectorized algorithms for spiking neural network simulation (Brette and Goodman 2011)

Re-display model names with descriptions