Command: iaf_psc_alpha_canon

Description

iaf_psc_alpha_canon is the "canonical" implementatoin of the leaky

integrate-and-fire model neuron with alpha-shaped postsynaptic

currents in the sense of [1]. This is the most exact implementation

available.

PSCs are normalized to an amplitude of 1pA.

The canonical implementation handles neuronal dynamics in a locally

event-based manner with in coarse time grid defined by the minimum

delay in the network see [1]. Incoming spikes are applied at the

precise moment of their arrival while the precise time of outgoing

spikes is determined by interpolation once a threshold crossing has

been detected. Return from refractoriness occurs precisly at spike

time plus refractory period.

This implementation is more complex than the plain iaf_psc_alpha

neuron but achieves much higher precision. In particular it does not

suffer any binning of spike times to grid points. Depending on your

application the canonical application may provide superior overall

performance given an accuracy goal; see [1] for details. Subthreshold

dynamics are integrated using exact integration between events [2].

Parameters

The following parameters can be set in the status dictionary.

V_m double - Membrane potential in mV

E_L double - Resting membrane potential in mV.

V_min double - Absolute lower value for the membrane potential.

C_m double - Capacity of the membrane in pF

tau_m double - Membrane time constant in ms.

t_ref double - Duration of refractory period in ms.

V_th double - Spike threshold in mV.

V_reset double - Reset potential of the membrane in mV.

tau_syn double - Rise time of the synaptic alpha function in ms.

I_e double - Constant external input current in pA.

Interpol_Order int - Interpolation order for spike time:

0-none 1-linear 2-quadratic 3-cubic

Author

Diesmann
Eppler
Morrison
Plesser
Straube

Sends

SpikeEvent

Receives

SpikeEvent
CurrentEvent
DataLoggingRequest

[1] Morrison A Straube S Plesser H E & Diesmann M (2006) Exact Subthreshold

Integration with Continuous Spike Times in Discrete Time Neural Network

Simulations. To appear in Neural Computation.

[2] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear

systems with applications to neuronal modeling. Biologial Cybernetics

81:381-402.

[3] Hanuschkin A Kunkel S Helias M Morrison A & Diesmann M (2010)

A general and efficient method for incorporating exact spike times in

globally time-driven simulations Front Neuroinformatics 4:113

References

[1] Morrison A Straube S Plesser H E & Diesmann M (2006) Exact Subthreshold

Integration with Continuous Spike Times in Discrete Time Neural Network

Simulations. To appear in Neural Computation.

[2] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear

systems with applications to neuronal modeling. Biologial Cybernetics

81:381-402.

[3] Hanuschkin A Kunkel S Helias M Morrison A & Diesmann M (2010)

A general and efficient method for incorporating exact spike times in

globally time-driven simulations Front Neuroinformatics 4:113

File

precise/iaf_psc_alpha_canon.h

If tau_m is very close to tau_syn_ex or tau_syn_in the model

will numerically behave as if tau_m is equal to tau_syn_ex or

tau_syn_in respectively to avoid numerical instabilities.

For details please see IAF_Neruons_Singularity.ipynb in

the NEST source code (docs/model_details).

Remarks

If tau_m is very close to tau_syn_ex or tau_syn_in the model

will numerically behave as if tau_m is equal to tau_syn_ex or

tau_syn_in respectively to avoid numerical instabilities.

For details please see IAF_Neruons_Singularity.ipynb in

the NEST source code (docs/model_details).