Wiki » History » Version 17
Version 16 (Vitor Chaud, 30 Apr 2014 14:57) → Version 17/45 (Vitor Chaud, 30 Apr 2014 14:57)
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** |
|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)|
|Inhibition-induced spiking| S |(a)| ©|
|Inhibition-induced bursting| T |(b) | (b)| (f)|
(a) Same behaviour
(b) Similar behaviour when slightly modifying parameters
© Similar but not identical behaviour (different number of spikes in the stimulus time frame)
(d) Not yet implemented. Need ramp injected current. See..
(e) Alternative model implementation had to be created since the model parameterization is different from the others
(f) Could not reproduce the behaviour seem in the original publication
------------
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** |
|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)|
|Inhibition-induced spiking| S |(a)| ©|
|Inhibition-induced bursting| T |(b) | (b)| (f)|
(a) Same behaviour
(b) Similar behaviour when slightly modifying parameters
© Similar but not identical behaviour (different number of spikes in the stimulus time frame)
(d) Not yet implemented. Need ramp injected current. See..
(e) Alternative model implementation had to be created since the model parameterization is different from the others
(f) Could not reproduce the behaviour seem in the original publication