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

This project deals with contains examples of the re-implementation of Izhikevich’s Izhikevich spiking neuron model (See [here](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1257420)). Currently, this model is supported by NeuroML 2 and PyNN (Neuron and NEST backends). Simulation results are model. Most of the behaviours seem in general equal or similar to those shown in the original publication (see Fig. 1 of [Izhikevich 2004](http://www.izhikevich.org/publications/whichmod.htm)). However, few model features 2004](http://www.izhikevich.org/publications/whichmod.htm) are difficult to reproduce due to particularities regarding model description and/or backend implementations, as further described below. 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**|&nbsp;&nbsp;&nbsp;&nbsp; **Label** &nbsp;&nbsp;&nbsp;&nbsp; |&nbsp;&nbsp;&nbsp;&nbsp; **PyNN** &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp;&nbsp;&nbsp;&nbsp; **NeuroML** &nbsp;&nbsp;&nbsp;&nbsp; |
|Tonic spiking | &nbsp;&nbsp;&nbsp;A |(a) | &nbsp;&nbsp;&nbsp;(a) |
|Phasic spiking| &nbsp;&nbsp;&nbsp;B |(a) | &nbsp;&nbsp;&nbsp;(a) |
|Tonic bursting| &nbsp;&nbsp;&nbsp;C |(b) | &nbsp;&nbsp;&nbsp;(b) |
|Phasic bursting| &nbsp;&nbsp;&nbsp;D |(a) | &nbsp;&nbsp;&nbsp;(a) |
|Mixed mode| &nbsp;&nbsp;&nbsp;E |(a) | &nbsp;&nbsp;&nbsp;(a) |
|Spike freq. adapt.| &nbsp;&nbsp;&nbsp;F |(a) | &nbsp;&nbsp;&nbsp;(a) |
|Class 1 excitable| &nbsp;&nbsp;&nbsp;G |(d, e)| &nbsp;&nbsp;&nbsp;(a, e) |
|Class 2 excitable| &nbsp;&nbsp;&nbsp;H |(d)| &nbsp;&nbsp;&nbsp;© |
|Spike latency | &nbsp;&nbsp;&nbsp;I |(b)| &nbsp;&nbsp;&nbsp;(b) |
|Subthresh. osc.| &nbsp;&nbsp;&nbsp;J |(a)| &nbsp;&nbsp;&nbsp;(a) |
|Resonator| &nbsp;&nbsp;&nbsp;K |(a)| &nbsp;&nbsp;&nbsp;(a) |
|Integrator| &nbsp;&nbsp;&nbsp;L |(e)| &nbsp;&nbsp;&nbsp;(a, e) |
|Rebound spike| &nbsp;&nbsp;&nbsp;M |(a)| &nbsp;&nbsp;&nbsp;(a) |
|Rebound burst| &nbsp;&nbsp;&nbsp;N |(a)| &nbsp;&nbsp;&nbsp;(a) |
|Threshold variability| &nbsp;&nbsp;&nbsp;O |(a)| &nbsp;&nbsp;&nbsp;(a) |
|Bistability| &nbsp;&nbsp;&nbsp;P |(b)| &nbsp;&nbsp;&nbsp;(b) |
|Depolarizing after-potential| &nbsp;&nbsp;&nbsp;Q |(b)| &nbsp;&nbsp;&nbsp;(b) |
|Accomodation| &nbsp;&nbsp;&nbsp;R |(d)| &nbsp;&nbsp;&nbsp;(a, f)|
|Inhibition-induced spiking| &nbsp;&nbsp;&nbsp;S |(a)| &nbsp;&nbsp;&nbsp;©|
|Inhibition-induced bursting| &nbsp;&nbsp;&nbsp;T |(b) | &nbsp;&nbsp;&nbsp;(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 |