iaf_psc_alpha_neuron
####################


iaf_psc_alpha - Leaky integrate-and-fire neuron model

Description
+++++++++++

iaf_psc_alpha is an implementation of a leaky integrate-and-fire model
with alpha-function kernel synaptic currents. Thus, synaptic currents
and the resulting post-synaptic potentials have a finite rise time.

The threshold crossing is followed by an absolute refractory period
during which the membrane potential is clamped to the resting potential.

The general framework for the consistent formulation of systems with
neuron like dynamics interacting by point events is described in
[1]_.  A flow chart can be found in [2]_.

Critical tests for the formulation of the neuron model are the
comparisons of simulation results for different computation step
sizes.

The iaf_psc_alpha is the standard model used to check the consistency
of the nest simulation kernel because it is at the same time complex
enough to exhibit non-trivial dynamics and simple enough compute
relevant measures analytically.

.. note::
   If tau_m is very close to tau_syn_exc or tau_syn_inh, numerical problems
   may arise due to singularities in the propagator matrics. If this is
   the case, replace equal-valued parameters by a single parameter.

   For details, please see ``IAF_neurons_singularity.ipynb`` in
   the NEST source code (``docs/model_details``).


References
++++++++++

.. [1] Rotter S,  Diesmann M (1999). Exact simulation of
       time-invariant linear systems with applications to neuronal
       modeling. Biologial Cybernetics 81:381-402.
       DOI: https://doi.org/10.1007/s004220050570
.. [2] 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.
       DOI: https://doi.org/10.1016/S0925-2312(01)00409-X
.. [3] Morrison A, Straube S, Plesser H E, Diesmann M (2006). Exact
       subthreshold integration with continuous spike times in discrete time
       neural network simulations. Neural Computation, in press
       DOI: https://doi.org/10.1162/neco.2007.19.1.47


See also
++++++++

iaf_psc_delta, iaf_psc_exp, iaf_cond_alpha



Parameters
++++++++++
.. csv-table::
    :header: "Name", "Physical unit", "Default value", "Description"
    :widths: auto

    
    "C_m", "pF", "250pF", "Capacitance of the membrane"    
    "tau_m", "ms", "10ms", "Membrane time constant"    
    "tau_syn_inh", "ms", "2ms", "Time constant of synaptic current"    
    "tau_syn_exc", "ms", "2ms", "Time constant of synaptic current"    
    "refr_T", "ms", "2ms", "Duration of refractory period"    
    "E_L", "mV", "-70mV", "Resting potential"    
    "V_reset", "mV", "-70mV", "Reset potential of the membrane"    
    "V_th", "mV", "-55mV", "Spike threshold potential"    
    "I_e", "pA", "0pA", "constant external input current"



State variables
+++++++++++++++

.. csv-table::
    :header: "Name", "Physical unit", "Default value", "Description"
    :widths: auto

    
    "V_m", "mV", "E_L", ""    
    "refr_t", "ms", "0ms", "Refractory period timer"    
    "is_refractory", "boolean", "false", ""




Equations
+++++++++



.. math::
   \frac{ dV_{m} } { dt }= \frac{ -(V_{m} - E_{L}) } { \tau_{m} } + \frac{ I } { C_{m} }



Source code
+++++++++++

The model source code can be found in the NESTML models repository here: `iaf_psc_alpha_neuron <https://github.com/nest/nestml/tree/master/models/neurons/iaf_psc_alpha_neuron.nestml>`_.

.. include:: iaf_psc_alpha_neuron_characterisation.rst


.. footer::

   Generated at 2024-05-22 14:51:14.609049