iaf_psc_alpha_presc


Name:
iaf_psc_alpha_presc - Leaky integrate-and-fire neuron  
with alpha-shape postsynaptic currents; prescient implementation.
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

Receives:
SpikeEvent, CurrentEvent, DataLoggingRequest  

Sends:
SpikeEvent  

Remarks:
 
If tau_m is very close to tau_syn, the model will numerically behave
as if tau_m is equal to tau_syn, to avoid numerical instabilities.
For details, please see IAF_Neruons_Singularity.ipynb in
the NEST source code (docs/model_details).

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.

Author:
Diesmann, Eppler, Morrison, Plesser, Straube  

SeeAlso: Source:
/home/graber/work-nest/nest-git/nest-simulator/precise/iaf_psc_alpha_presc.h