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. Note that subplots G, H, L and R are not in accordance with the original published results (see Comparison to original model behavior).

![](fig1_pyNN_nest.png)

#### 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 |    A |(a) |    (a) |    (a) |

|Phasic spiking|    B |(a) |    (a) |   (a) |

|Tonic bursting|    C |(b) |    (b) |   (b) |

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

|Mixed mode|    E |(a) |    (a) |   (a) |

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

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

|Class 2 excitable|    H |©|    (d) |    (g) |

|Spike latency |    I |(b)|    (b) |   (b) |

|Subthresh. osc.|    J |(a)|    (a) |   (a) |

|Resonator|    K |(a)|    (a) |   (a) |

|Integrator|    L |(a, e)|    (e) |   (e) |

|Rebound spike|    M |(a)|    (a) |    (a) |

|Rebound burst|    N |(a)|    (a) |   (a) |

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

|Bistability|    P |(b)|    (b) |    (b) |

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

|Accomodation|    R |(a, f)|    (d)|   (f)|

|Inhibition-induced spiking|    S |(b)|    (b)|   (b)|

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

![](moose_gui.png)