Command: iaf_cond_beta

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Name:
iaf_cond_beta - Simple conductance based leaky integrate-and-fire neuron
model.
Description:
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].
Parameters:
The following parameters can be set in the status dictionary.

V_m double - Membrane potential in mV
E_L double - Leak reversal potential in mV.
C_m double - Capacity of the membrane in pF
t_ref double - Duration of refractory period in ms.
V_th double - Spike threshold in mV.
V_reset double - Reset potential of the membrane in mV.
E_ex double - Excitatory reversal potential in mV.
E_in double - Inhibitory reversal potential in mV.
g_L double - Leak conductance in nS;
tau_rise_ex double - Rise time of the excitatory synaptic beta function in ms.
tau_decay_ex double - Rise time of the excitatory synaptic beta function in ms.
tau_rise_in double - Rise time of the inhibitory synaptic beta function in ms.
tau_decay_in double - Rise time of the inhibitory synaptic beta function in ms.
I_e double - Constant input current in pA.
Require:
HAVE_GSL
Receives:
SpikeEvent, CurrentEvent, DataLoggingRequest
Sends:
SpikeEvent
Remarks:
• 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.
References:
Meffin, H., Burkitt, A. N., & Grayden, D. B. (2004). An analytical
model for the large, fluctuating synaptic conductance state typical of
neocortical neurons in vivo. J. Comput. Neurosci., 16, 159-175.

Bernander, O ., Douglas, R. J., Martin, K. A. C., & Koch, C. (1991).
Synaptic background activity influences spatiotemporal integration in
single pyramidal cells. Proc. Natl. Acad. Sci. USA, 88(24),
11569-11573.

Kuhn, Aertsen, Rotter (2004) Neuronal Integration of Synaptic Input in
the Fluctuation- Driven Regime. Jneurosci 24(10) 2345-2356

Rotter & Diesmann, Biol Cybern 81:381 (1999)

Roth and van Rossum,
Ch 6, in De Schutter, Computational Modeling Methods for Neuroscientists,
MIT Press, 2010.
Author:
Daniel Naoumenko (modified iaf_cond_alpha by Schrader, Plesser)
SeeAlso:
Source:
/usr/src/packages/BUILD/models/iaf_cond_beta.h
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