iaf_psc_exp_ps - Leaky integrate-and-fire neuronDescription:
with exponential postsynaptic currents; canoncial implementation;
bisectioning method for approximation of threshold crossing.
iaf_psc_exp_ps is the "canonical" implementation of the leaky
integrate-and-fire model neuron with exponential postsynaptic currents
that uses the bisectioning method to approximate the timing of a threshold
crossing [1,2]. This is the most exact implementation available.
The canonical implementation handles neuronal dynamics in a locally
event-based manner with in coarse time grid defined by the minimum
delay in the network, see [1,2]. Incoming spikes are applied at the
precise moment of their arrival, while the precise time of outgoing
spikes is determined by bisectioning once a threshold crossing has
been detected. Return from refractoriness occurs precisely at spike
time plus refractory period.
This implementation is more complex than the plain iaf_psc_exp
neuron, but achieves much higher precision. In particular, it does not
suffer any binning of spike times to grid points. Depending on your
application, the canonical application with bisectioning may provide
superior overall performance given an accuracy goal; see [1,2] for
details. Subthreshold dynamics are integrated using exact integration
between events .
The following parameters can be set in the status dictionary.
E_L double - Resting membrane potential in mV.
C_m double - Capacitance of the membrane in pF.
tau_m double - Membrane time constant in ms.
tau_syn_ex double - Excitatory synaptic time constant in ms.
tau_syn_in double - Inhibitory synaptic time constant in ms.
t_ref double - Duration of refractory period in ms.
V_th double - Spike threshold in mV.
I_e double - Constant input current in pA.
V_min double - Absolute lower value for the membrane potential in mV.
V_reset double - Reset value for the membrane potential in mV.
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_Neruons_Singularity.ipynb in the
NEST source code (docs/model_details).
 Morrison A, Straube S, Plesser HE & Diesmann M (2007) Exact subthreshold
integration with continuous spike times in discrete time neural network
simulations. Neural Comput 19, 47-79
 Hanuschkin A, Kunkel S, Helias M, Morrison A and Diesmann M (2010) A
general and efficient method for incorporating precise spike times in
globally timedriven simulations. Front Neuroinform 4:113
 Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
systems with applications to neuronal modeling. Biol Cybern 81:381-402