Circuits that contain the Cell : Abstract Izhikevich neuron

("We present a model that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC." (Izhikevich 2003))
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    Models
1. A spiking neural network model of the Lateral Geniculate Nucleus (Sen-Bhattacharya et al 2017)
2. CA1 pyramidal neuron network model (Ferguson et al 2015)
3. Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016)
4. Excitotoxic loss of dopaminergic cells in PD (Muddapu et al 2019)
5. Gap junction plasticity as a mechanism to regulate network-wide oscillations (Pernelle et al 2018)
6. Input strength and time-varying oscillation peak frequency (Cohen MX 2014)
7. Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
8. Norns - Neural Network Studio (Visser & Van Gils 2014)
9. Parallelizing large networks in NEURON (Lytton et al. 2016)
10. Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)
11. Supervised learning in spiking neural networks with FORCE training (Nicola & Clopath 2017)

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