| Name: | iaf_tum_2000 - Leaky integrate-and-fire neuron model with exponential PSCs.
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| 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].
|
| Remarks: |
The present implementation uses individual variables for the
components of the state vector and the non-zero matrix elements of
the propagator. Because the propagator is a lower triangular matrix
no full matrix multiplication needs to be carried out and the
computation can be done "in place" i.e. no temporary state vector
object is required.
The template support of recent C++ compilers enables a more succinct
formulation without loss of runtime performance already at minimal
optimization levels. A future version of iaf_tum_2000 will probably
address the problem of efficient usage of appropriate vector and
matrix objects.
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| 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.
Note:
tau_m != tau_syn_{ex,in} is required by the current implementation to avoid a
degenerate case of the ODE describing the model [1]. For very similar values,
numerics will be unstable.
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| 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.
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| Sends: | SpikeEvent
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| Receives: | SpikeEvent, CurrentEvent, DataLoggingRequest
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| FirstVersion: | March 2006
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| Author: | Moritz Helias
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| Source: | /home/abuild/rpmbuild/BUILD/nest-2.4.1/models/iaf_tum_2000.h
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