iaf_cond_beta - Simple conductance based leaky integrate-and-fire neuron
model.
iaf_cond_beta is an implementation of a spiking neuron using IAF dynamics with
conductance-based synapses. Incoming spike events induce a post-synaptic change
of conductance modelled by an beta function. The beta function
is normalised such that an event of weight 1.0 results in a peak current of
1 nS at t = tau_rise_[ex|in].
The following parameters can be set in the status dictionary.
\verbatim embed:rst
============= ====== =========================================================
V_m mV Membrane potential
E_L mV Leak reversal potential
C_m pF Capacity of the membrane
t_ref ms Duration of refractory period
V_th mV Spike threshold
V_reset mV Reset potential 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_decay_ex ms Rise time of the excitatory synaptic beta function
tau_syn_in ms Rise time of the inhibitory synaptic alpha function
tau_decay_in ms Rise time of the inhibitory synaptic beta function
I_e pA Constant input current
============= ====== =========================================================
\endverbatim
SpikeEvent, CurrentEvent, DataLoggingRequest
SpikeEvent
• Per 2009-04-17, this class has been revised to our newest
insights into class design. Please use THIS CLASS as a reference
when designing your own models with nonlinear dynamics.
One weakness of this class is that it distinguishes between
inputs to the two synapses by the sign of the synaptic weight.
It would be better to use receptor_types, cf iaf_cond_alpha_mc.
\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
.. [3] Kuhn A, Rotter S (2004) Neuronal integration of synaptic input in
the fluctuation- driven regime. Journal of Neuroscience,
24(10):2345-2356
DOI: https://doi.org/10.1523/JNEUROSCI.3349-03.2004
.. [4] 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
.. [5] Roth A and van Rossum M (2010). Chapter 6: Modeling synapses.
in De Schutter, Computational Modeling Methods for Neuroscientists,
MIT Press.
\endverbatim
Daniel Naoumenko (modified iaf_cond_alpha by Schrader, Plesser)
/var/www/debian/nest/nest-simulator-2.18.0/models/iaf_cond_beta.h