Command: iaf_tum_2000

Description

iaf_tum_2000 is an implementation of a leaky integrate-and-fire model

with exponential shaped postsynaptic currents (PSCs) according to [1].

The postsynaptic currents have an infinitely short rise time.

In particular this model allows setting an absolute and relative

refractory time separately as requied by [1].

The threshold crossing is followed by an absolute refractory period (tau_abs)

during which the membrane potential is clamped to the resting potential.

During the total refractory period the membrane potential evolves

but the neuron will not emit a spike even if the membrane potential

reaches threshold. The total refratory time must be larger or equal to

the absolute refractory time. If equal the refractoriness of the model

if equivalent to the other models of NEST.

The linear subthresold dynamics is integrated by the Exact

Integration scheme [2]. The neuron dynamics is solved on the time

grid given by the computation step size. Incoming as well as emitted

spikes are forced to that grid.

An additional state variable and the corresponding differential

equation represents a piecewise constant external current.

The general framework for the consistent formulation of systems with

neuron like dynamics interacting by point events is described in

[2]. A flow chart can be found in [3].

Parameters

The following parameters can be set in the status dictionary.

E_L double - Resting membrane potential in mV.

C_m double - Capacity of the membrane in pF

tau_m double - Membrane time constant in ms.

tau_syn_ex double - Time constant of postsynaptic excitatory currents in ms

tau_syn_in double - Time constant of postsynaptic inhibitory currents in ms

t_ref_abs double - Duration of absolute refractory period (V_m = V_reset) in ms.

t_ref_tot double - Duration of total refractory period (no spiking) in ms.

V_m double - Membrane potential in mV

V_th double - Spike threshold in mV.

V_reset double - Reset membrane potential after a spike in mV.

I_e double - Constant input current in pA.

t_spike double - Point in time of last spike in ms.

Author

Moritz Helias

Sends

SpikeEvent

Receives

SpikeEvent
CurrentEvent
DataLoggingRequest

[1] Misha Tsodyks Asher Uziel and Henry Markram (2000) Synchrony Generation in Recurrent

Networks with Frequency-Dependent Synapses The Journal of Neuroscience 2000 Vol. 20 RC50 p.

1-5

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

systems with applications to neuronal modeling. Biologial Cybernetics

81:381-402.

[3] Diesmann M Gewaltig M-O Rotter S & Aertsen A (2001) State space

analysis of synchronous spiking in cortical neural networks.

Neurocomputing 38-40:565-571.

References

[1] Misha Tsodyks Asher Uziel and Henry Markram (2000) Synchrony Generation in Recurrent

Networks with Frequency-Dependent Synapses The Journal of Neuroscience 2000 Vol. 20 RC50 p.

1-5

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

systems with applications to neuronal modeling. Biologial Cybernetics

81:381-402.

[3] Diesmann M Gewaltig M-O Rotter S & Aertsen A (2001) State space

analysis of synchronous spiking in cortical neural networks.

Neurocomputing 38-40:565-571.

File

models/iaf_tum_2000.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).

FirstVersion