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 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 or PhasicBursting

Tested with simulators: …

Comparison to original model behaviour¶

table{border:1px solid black}.
{background:#ddd}. |Model|Label |PyNN |NeuroML | Comments |
|Tonic spiking |    A      |OK |    OK ||
|Phasic spiking|    B |OK |    OK ||
|Tonic bursting|    C |OK |    OK ||
|Phasic bursting|    D |OK |    OK ||
|Mixed mode|    E |OK |    OK ||
|Spike freq. adapt.|    F |OK |    OK ||
|Class 1 excitable|    G |not yet implemented| OK (although new ComponentType generalizedIzhikevichCell required)| Different model parameterization |
|Class 2 excitable|    H |not yet implemented|    OK ||
|Spike latency |    I |OK|    OK| Requires modified pulse amplitude for more pronounced spike latency at smaller dt |
|Subthresh. osc.|    J |OK|    OK| |
|Resonator|    K |not yet implemented|    OK ||
|Integrator|    L |not yet implemented|    OK (although new ComponentType generalizedIzhikevichCell required)| Different model parameterization|
|Rebound spike|    M |OK|    OK ||
|Rebound burst|    N |OK|    OK ||
|Threshold variability|    O |not yet implemented|    OK ||
|Bistability|    P |OK|    OK | Requires a slightly altered time of 2nd spike to ensure bistability at smaller dt |
|Depolarizing after-potential|    Q |PROBLEM|    PROBLEM| Response depending on the time step|
|Accomodation|    R |not yet implemented|    PROBLEM| Different from fig1|
|Inhibition-induced spiking|    S |PROBLEM|    PROBLEM| Response depending on the time step|
|Inhibition-induced bursting|    T |PROBLEM |    PROBLEM| Response diverging on PyNN and depending on the time step on NeuroML|