Warning

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_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 postsynaptic change of conductance modelled by a beta function. The beta function is normalized such that an event of weight 1.0 results in a peak current of 1 nS at \(t = \tau_{rise\_[ex|in]}\).

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.

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_rise_ex

ms

Rise time of the excitatory synaptic beta function

tau_decay_ex

ms

Decay time of the excitatory synaptic beta function

tau_rise_in

ms

Rise time of the inhibitory synaptic beta function

tau_decay_in

ms

Decay time of the inhibitory synaptic beta function

I_e

pA

Constant input current

Sends

SpikeEvent

Receives

SpikeEvent, CurrentEvent, DataLoggingRequest

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

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.