iaf_tum_2000 - Leaky integrate-and-fire neuron model with exponentialDescription:
iaf_tum_2000 is an implementation of a leaky integrate-and-fire model
with exponential shaped postsynaptic currents (PSCs) according to .
The postsynaptic currents have an infinitely short rise time.
In particular, this model allows setting an absolute and relative
refractory time separately, as required by .
The threshold crossing is followed by an absolute refractory period
(t_ref_abs) during which the membrane potential is clamped to the resting
potential. During the total refractory period (t_ref_tot), the membrane
potential evolves, but the neuron will not emit a spike, even if the
membrane potential reaches threshold. The total refractory 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 subthreshold dynamics is integrated by the Exact
Integration scheme . 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
. A flow chart can be found in .
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)
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.
SpikeEvent, CurrentEvent, DataLoggingRequestSends:
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_neurons_singularity.ipynb in
the NEST source code (docs/model_details).
 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
 Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
systems with applications to neuronal modeling. Biologial Cybernetics
 Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001) State space
analysis of synchronous spiking in cortical neural networks.