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

This project deals with the re-implementation of Izhikevich’s 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 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 are difficult to reproduce due to particularities regarding model description and/or backend implementations, as further described below.

### 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

In order to install PyNN see http://neuralensemble.org/trac/PyNN/wiki/Installation. Preferably, use the latest v0.8 version from [GitHub](https://github.com/NeuralEnsemble/PyNN). At the moment, the model is supported by Neuron and NEST backend simulators.

To perform simulations using [NeuroML2](https://github.com/NeuroML/jNeuroML) and [LEMS](https://github.com/LEMS/jLEMS) you may install a pre-compiled package named jNeuroML as described [here](http://sourceforge.net/mailarchive/forum.php?thread_name=51C96757.8090601%40ucl.ac.uk&forum_name=neuroml-technology).

### 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

##### Simulating Fig. 1 protocol in PyNN

First, go to the PyNN subdirectory in your working directory:

> cd IzhikevichModel/PyNN/

Then, type the following command to run a simulation using Neuron:

> python izhikevich2004.py neuron

… or to run a simulation using NEST:

> python izhikevich2004.py nest

The figure below shows the result obtained when running the current version of izhikevich2004.py with NEST.


#### NeuroML 2

First, go to the NeuroML2 subdirectory in your working directory:

> cd IzhikevichModel/NeuroML2/

Then, type the following command to run a simulation using LEMS:

> ../../jNeuroMLJar/jnml LEMS_WhichModel.xml

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)

Comparison to original model behavior
-------------------------------------

table{border:1px solid black}.
{background:\#ddd}. |**Model**|**Label** | **NeuroML 2** |**pyNN.neuron**| **pyNN.nest**|
|Tonic spiking | &nbsp;&nbsp;&nbsp;A |(a) | &nbsp;&nbsp;&nbsp;(a) | &nbsp;&nbsp;&nbsp;(a) |
|Phasic spiking| &nbsp;&nbsp;&nbsp;B |(a) | &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Tonic bursting| &nbsp;&nbsp;&nbsp;C |(b) | &nbsp;&nbsp;&nbsp;(b) |&nbsp;&nbsp;&nbsp;(b) |
|Phasic bursting| &nbsp;&nbsp;&nbsp;D |(a) | &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Mixed mode| &nbsp;&nbsp;&nbsp;E |(a) | &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Spike freq. adapt.| &nbsp;&nbsp;&nbsp;F |(a) | &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Class 1 excitable| &nbsp;&nbsp;&nbsp;G |(a, e)| &nbsp;&nbsp;&nbsp;(d, e) | &nbsp;&nbsp;&nbsp;(e) |
|Class 2 excitable| &nbsp;&nbsp;&nbsp;H |©| &nbsp;&nbsp;&nbsp;(d) | &nbsp;&nbsp;&nbsp;(g) |
|Spike latency | &nbsp;&nbsp;&nbsp;I |(b)| &nbsp;&nbsp;&nbsp;(b) |&nbsp;&nbsp;&nbsp;(b) |
|Subthresh. osc.| &nbsp;&nbsp;&nbsp;J |(a)| &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Resonator| &nbsp;&nbsp;&nbsp;K |(a)| &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Integrator| &nbsp;&nbsp;&nbsp;L |(a, e)| &nbsp;&nbsp;&nbsp;(e) |&nbsp;&nbsp;&nbsp;(e) |
|Rebound spike| &nbsp;&nbsp;&nbsp;M |(a)| &nbsp;&nbsp;&nbsp;(a) | &nbsp;&nbsp;&nbsp;(a) |
|Rebound burst| &nbsp;&nbsp;&nbsp;N |(a)| &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Threshold variability| &nbsp;&nbsp;&nbsp;O |(a)| &nbsp;&nbsp;&nbsp;(a) |&nbsp;&nbsp;&nbsp;(a) |
|Bistability| &nbsp;&nbsp;&nbsp;P |(b)| &nbsp;&nbsp;&nbsp;(b) | &nbsp;&nbsp;&nbsp;(b) |
|Depolarizing after-potential| &nbsp;&nbsp;&nbsp;Q |(b)| &nbsp;&nbsp;&nbsp;(b) |&nbsp;&nbsp;&nbsp;(b) |
|Accomodation| &nbsp;&nbsp;&nbsp;R |(a, f)| &nbsp;&nbsp;&nbsp;(d)|&nbsp;&nbsp;&nbsp;(f)|
|Inhibition-induced spiking| &nbsp;&nbsp;&nbsp;S |(b)| &nbsp;&nbsp;&nbsp;(b)|&nbsp;&nbsp;&nbsp;(b)|
|Inhibition-induced bursting| &nbsp;&nbsp;&nbsp;T |(b) | &nbsp;&nbsp;&nbsp;(b)|&nbsp;&nbsp;&nbsp;(b)|

(a) Same behavior
(b) Similar behavior when slightly modifying parameters. See the table below.
© Similar but not identical behavior (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.
(g) Could not reproduce model behavior

### Parameter changes to adequate model behavior

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 |

Alternative implementations
---------------------------

An alternative implementation of the Izhikevich model was created using [Moose](http://moose.sourceforge.net/). The code can be found [here](http://sourceforge.net/p/moose/code/4733/tree/moose/branches/buildQ/Demos/izhikevich/). There is a GUI in which the user chooses the model parameterization an visualizes the simulation results (see the figure below).