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.

See also [1], [2], [3], [4], [5].

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

See also

Neuron, Integrate-And-Fire, Conductance-Based

Examples using this model

None