Introduction
------------

This project will contain examples of the Izhikevich spiking neuron model.

### Installation

To get local clone of this project…

### 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

…

Tested with simulators: 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** | **Comments** |
|Tonic spiking | &nbsp;&nbsp;&nbsp;A&nbsp; |A&nbsp; &nbsp; &nbsp; |OK |OK | &nbsp;&nbsp;&nbsp;OK |
|Phasic spiking| &nbsp;&nbsp;&nbsp;B |B |OK |OK | &nbsp;&nbsp;&nbsp;OK |
|Tonic bursting| &nbsp;&nbsp;&nbsp;C |C |OK |OK | &nbsp;&nbsp;&nbsp;OK |
|Phasic bursting| &nbsp;&nbsp;&nbsp;D |D |OK |OK | &nbsp;&nbsp;&nbsp;OK |
|Mixed mode| &nbsp;&nbsp;&nbsp;E |E |OK |OK | &nbsp;&nbsp;&nbsp;OK |
|Spike freq. adapt.| &nbsp;&nbsp;&nbsp;F |F |OK |OK | &nbsp;&nbsp;&nbsp;OK |
|Class 1 excitable| &nbsp;&nbsp;&nbsp;G |G |not yet implemented| &nbsp;&nbsp;&nbsp;PROBLEM| &nbsp;&nbsp;&nbsp;Different problem: different model parameterization|
|Class 2 excitable| &nbsp;&nbsp;&nbsp;H |H |not yet implemented| &nbsp;&nbsp;&nbsp;OK OK |
|Spike latency | &nbsp;&nbsp;&nbsp;I |I |OK| &nbsp;&nbsp;&nbsp;OK| OK|
|Subthresh. osc.| &nbsp;&nbsp;&nbsp;J |J |OK| &nbsp;&nbsp;&nbsp;OK| OK|
|Resonator| &nbsp;&nbsp;&nbsp;K |K |not yet implemented| &nbsp;&nbsp;&nbsp;OK OK |
|Integrator| &nbsp;&nbsp;&nbsp;L |L |not yet implemented| &nbsp;&nbsp;&nbsp;PROBLEM| &nbsp;&nbsp;&nbsp;Different problem different model parameterization|
|Rebound spike| &nbsp;&nbsp;&nbsp;M |M |OK| &nbsp;&nbsp;&nbsp;OK OK |
|Rebound burst| &nbsp;&nbsp;&nbsp;N |N |OK| &nbsp;&nbsp;&nbsp;OK OK |
|Threshold variability| &nbsp;&nbsp;&nbsp;O |O |not yet implemented| &nbsp;&nbsp;&nbsp;OK OK |
|Bistability| &nbsp;&nbsp;&nbsp;P |P |PROBLEM| &nbsp;&nbsp;&nbsp;PROBLEM problem |
|Depolarizing after-potential| &nbsp;&nbsp;&nbsp;Q |Q |PROBLEM| &nbsp;&nbsp;&nbsp;PROBLEM| &nbsp;&nbsp;&nbsp;Response problem response depending on the time step|
|Accomodation| &nbsp;&nbsp;&nbsp;R |R |not yet implemented| &nbsp;&nbsp;&nbsp;PROBLEM| &nbsp;&nbsp;&nbsp;Different PROBLEM different from fig1|
|Inhibition-induced spiking| &nbsp;&nbsp;&nbsp;S |S |PROBLEM| &nbsp;&nbsp;&nbsp;PROBLEM| &nbsp;&nbsp;&nbsp;Response PROBLEM response depending on the time step|
|Inhibition-induced bursting| &nbsp;&nbsp;&nbsp;T |T |PROBLEM | &nbsp;&nbsp;&nbsp;PROBLEM| &nbsp;&nbsp;&nbsp;Response diverging on PyNN and (diverg.)| PROBLEM response depending on the time step on NeuroML| step|