iaf_cond_alpha_mc - PROTOTYPE Multi-compartment conductance-based leaky
integrate-and-fire neuron model.
THIS MODEL IS A PROTOTYPE FOR ILLUSTRATION PURPOSES. IT IS NOT YET
FULLY TESTED. USE AT YOUR OWN PERIL!
iaf_cond_alpha_mc is an implementation of a multi-compartment spiking
neuron using IAF dynamics with conductance-based synapses. It serves
mainly to illustrate the implementation of multicompartment models in
NEST.
The model has three compartments: soma, proximal and distal dendrite,
labeled as s, p, and d, respectively. Compartments are connected through
passive conductances as follows
@f[
C_{m.s} d/dt V_{m.s} = \ldots - g_{sp} ( V_{m.s} - V_{m.p} ) \\
C_{m.p} d/dt V_{m.p} = \ldots - g_{sp} ( V_{m.p} - V_{m.s} )
• g_{pd} ( V_{m.p} - V_{m.d} ) \\
C_{m.d} d/dt V_{m.d} = \ldots \qquad - g_{pd} ( V_{m.d} - V_{m.p} )
@f]
A spike is fired when the somatic membrane potential exceeds threshold,
\f$ V_{m.s} >= V_{th} \f$. After a spike, somatic membrane potential is
clamped to a reset potential, \f$ V_{m.s} == V_{reset} \f$, for the refractory
period. Dendritic membrane potentials are not manipulated after a spike.
There is one excitatory and one inhibitory conductance-based synapse
onto each compartment, with alpha-function time course. The alpha
function is normalised such that an event of weight 1.0 results in a
peak current of 1 nS at t = tau_syn. Each compartment can also receive
current input from a current generator, and an external (rheobase)
current can be set for each compartment.
Synapses, including those for injection external currents, are addressed through
the receptor types given in the receptor_types entry of the state dictionary.
Note that in contrast to the single-compartment iaf_cond_alpha model, all
synaptic weights must be positive numbers!
The following parameters can be set in the status dictionary. Parameters
for each compartment are collected in a sub-dictionary; these sub-dictionaries
are called "soma", "proximal", and "distal", respectively. In the list below,
these parameters are marked with an asterisk.
\verbatim embed:rst
============ ======= ==========================================================
V_m* mV Membrane potential
E_L* mV Leak reversal potential
C_m* pF Capacity of the membrane
E_ex* mV Excitatory reversal potential
E_in* mV Inhibitory reversal potential
g_L* nS Leak conductance
tau_syn_ex* ms Rise time of the excitatory synaptic alpha function
tau_syn_in* ms Rise time of the inhibitory synaptic alpha function
I_e* pA Constant input current
g_sp nS Conductance connecting soma and proximal dendrite
g_pd nS Conductance connecting proximal and distal dendrite
t_ref ms Duration of refractory period
V_th mV Spike threshold in mV
V_reset mV Reset potential of the membrane
============ ======= ==========================================================
\endverbatim
Example:
See pynest/examples/mc_neuron.py.
SpikeEvent, CurrentEvent, DataLoggingRequest
SpikeEvent
This is a prototype for illustration which has undergone only limited
testing. Details of the implementation and user-interface will likely
change. USE AT YOUR OWN PERIL!
• All parameters that occur for both compartments
and dendrite are stored as C arrays, with index 0 being soma.
\verbatim embed:rst
.. [1] Meffin H, Burkitt AN, Grayden DB (2004). An analytical
model for the large, fluctuating synaptic conductance state typical of
neocortical neurons in vivo. Journal of Computational Neuroscience,
16:159-175.
DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81
.. [2] Bernander O, Douglas RJ, Martin KAC, Koch C (1991). Synaptic background
activity influences spatiotemporal integration in single pyramidal
cells. Proceedings of the National Academy of Science USA,
88(24):11569-11573.
DOI: https://doi.org/10.1073/pnas.88.24.11569
\endverbatim
Plesser
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