Command: iaf_psc_alpha_presc

Description

iaf_psc_alpha_presc is the "prescient" implementation of the leaky

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

currents in the sense of [1].

PSCs are normalized to an amplitude of 1pA.

The prescient implementation predicts the effect of spikes arriving

during a time step by exactly integrating their effect from the

precise time of spike arrival to the end of the time step. This is

exact if the neuron was not refractory at the beginning of the

interval and remains subthreshold throughout the

interval. 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.

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.

File

precise/iaf_psc_alpha_presc.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).