Introduction

------------

This project contains examples of the Izhikevich spiking neuron model. Most of the behaviours seem in Fig. 1 of [Izhikevich 2004](http://www.izhikevich.org/publications/whichmod.htm) are reproduced in PyNN and NeuroML 2 implementations.

### Installation

To get local clone of this project [Install Git](http://www.opensourcebrain.org/projects/gitintro/wiki/Wiki), go to the directory in which the project will be cloned and type:

    > git clone https://github.com/OpenSourceBrain/IzhikevichModel.git

### Versions of the project

The original model in [MATLAB format](http://izhikevich.org/publications/figure1.m) has been converted to a number of other formats.

#### PyNN

Install [PyNN](http://neuralensemble.org/trac/PyNN/wiki/Installation). Preferably, use the latest v0.8 version from [GitHub](https://github.com/NeuralEnsemble/PyNN). Note: NEURON is the only supported simulator for this model at the moment.

    > cd IzhikevichModel/PyNN/

To run a simulation type:

    > python izhikevich2004.py neuron

#### NeuroML 2

…

The XML for an Izhikevich model in NeuroML v2.0 is below:

    <code class="xml">

    <izhikevichCell id="TonicSpiking" v0 = "-70mV" thresh = "30mV" a ="0.02" b = "0.2" c = "-65.0" d = "6" Iamp="0" Idel="0ms" Idur="2000ms"/></code>

For full examples of single cells see [TonicSpiking](/projects/izhikevichmodel/repository/entry/neuroConstruct/cellMechanisms/TonicSpiking/TonicSpiking.nml) or [PhasicBursting](/projects/izhikevichmodel/repository/entry/neuroConstruct/cellMechanisms/PhasicBursting/PhasicBursting.nml)

Tested with simulators: …

### Comparison to original model behaviour

table{border:1px solid black}.

{background:\#ddd}. |**Model**|     **Label**      |     **PyNN**      |      **NeuroML**      |

|Tonic spiking |    A |(a) |    (a) |

|Phasic spiking|    B |(a) |    (a) |

|Tonic bursting|    C |(b) |    (b) |

|Phasic bursting|    D |(a) |    (a) |

|Mixed mode|    E |(a) |    (a) |

|Spike freq. adapt.|    F |(a) |    (a) |

|Class 1 excitable|    G |(d, e)|    (a, e) |

|Class 2 excitable|    H |(d)|    © |

|Spike latency |    I |(b)|    (b) |

|Subthresh. osc.|    J |(a)|    (a) |

|Resonator|    K |(a)|    (a) |

|Integrator|    L |(e)|    (a, e) |

|Rebound spike|    M |(a)|    (a) |

|Rebound burst|    N |(a)|    (a) |

|Threshold variability|    O |(a)|    (a) |

|Bistability|    P |(b)|    (b) |

|Depolarizing after-potential|    Q |(b)|    (b) |

|Accomodation|    R |(d)|    (a, f)|

|Inhibition-induced spiking|    S |(a)|    ©|

|Inhibition-induced bursting|    T |(b) |    (b)|

(a) Same behaviour

(b) Similar behaviour when slightly modifying parameters. See the table below.

© Similar but not identical behaviour (different number of spikes in the stimulus time frame)

(d) Not yet implemented. Need ramp injected current. See https://github.com/NeuralEnsemble/PyNN/issues/257

(e) Requires an alternative model implementation since the model parameterization is different in the original Matlab code. In NeuroML new ComponentType [generalizedIzhikevichCell](https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NeuroML2/GeneralizedIzhikevichCell.xml) was created.

(f) Requires an alternative model implementation since the model parameterization is different in the original Matlab code. In NeuroML new ComponentType [accomodationIzhikevichCell](https://github.com/OpenSourceBrain/IzhikevichModel/blob/master/NeuroML2/GeneralizedIzhikevichCell.xml) was created.

#### Parameter changes to adequate model behaviour

table{border:1px solid black}.

{background:\#ddd}. |**Model**| **Label** | **Parameter**|**Original value**|**New value**|

|Spike latency | I | Amplitude of pulse current | 7.04 | 6.71 |

|Bistability | P | Initial time of 2nd pulse | 216 | 208 |

|Depolarizing after-potential | Q | b | 0.2 | 0.18 |

|Inhibition-induced spiking | S | Inhibition ending | 250 | 220 |

|Inhibition-induced bursting | T | d | ~~2.0 |~~0.7 |