Introduction¶

This project contains examples of the Izhikevich spiking neuron model. Most of the behaviours seem in Fig. 1 of Izhikevich 2004 are reproduced in PyNN and NeuroML 2 implementations.

Installation¶

To get local clone of this project Install Git, 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 has been converted to a number of other formats.

PyNN¶

Install PyNN. Preferably, use the latest v0.8 version from GitHub. 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 or PhasicBursting

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, f)|
|Inhibition-induced spiking|    S |(a)|    ©|
|Inhibition-induced bursting|    T |(b) |    (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 was created.
(f) Requires an alternative model implementation since the model parameterization is different in the original Matlab code. In NeuroML new ComponentType accomodationIzhikevichCell 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 |