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This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

iaf_cond_alpha – Simple conductance based leaky integrate-and-fire neuron model

Description

iaf_cond_alpha is an implementation of a spiking neuron using IAF dynamics with conductance-based synapses. Incoming spike events induce a postsynaptic change of conductance modelled by an alpha function. The alpha function is normalized such that an event of weight 1.0 results in a peak current of 1 nS at \(t = \tau_{syn}\).

Parameters

The following parameters can be set in the status dictionary.

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_syn_in

ms

Rise time of the inhibitory synaptic alpha function

I_e

pA

Constant input current

Sends

SpikeEvent

Receives

SpikeEvent, CurrentEvent, DataLoggingRequest

Remarks:

@note 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

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