iaf_psc_alpha_ps – Current-based leaky integrate-and-fire neuron with alpha-shaped postsynaptic currents using regula falsi method for approximation of threshold crossing

Description

New in version 2.18.

iaf_psc_alpha_ps is the “canonical” implementation of the leaky integrate-and-fire model neuron with alpha-shaped postsynaptic currents in the sense of [1]. This is the most exact implementation available.

PSCs are normalized to an amplitude of 1pA.

The precise implementation handles neuronal dynamics in a locally event-based manner with in coarse time grid defined by the minimum delay in the network, see [1]. Incoming spikes are applied at the precise moment of their arrival, while the precise time of outgoing spikes is determined by a Regula Falsi method to approximate the timing of a threshold crossing [1] [3]. Return from refractoriness occurs precisely at spike time plus refractory period.

This implementation is more complex than the plain iaf_psc_alpha neuron, but achieves much higher precision. In particular, it does not suffer any binning of spike times to grid points. Depending on your application, the canonical application may provide superior overall performance given an accuracy goal; see [1] for details. Subthreshold dynamics are integrated using exact integration between events [2].

This model transmits precise spike times to target nodes (on-grid spike time and offset). If this node is connected to a spike_recorder, the property “precise_times” of the spike_recorder has to be set to true in order to record the offsets in addition to the on-grid spike times.

The iaf_psc_alpha_ps neuron accepts connections transmitting CurrentEvents. These events transmit stepwise-constant currents which can only change at on-grid times.

Note

If tau_m is very close to tau_syn_ex or tau_syn_in, the model will numerically behave as if tau_m is equal to tau_syn_ex or tau_syn_in, respectively, to avoid numerical instabilities.

For implementation details see the IAF_neurons_singularity notebook.

For details about exact subthreshold integration, please see Integrating neural models using exact integration.

Parameters

The following parameters can be set in the status dictionary.

V_m

mV

Membrane potential

E_L

mV

Resting membrane potential

V_min

mV

Absolute lower value for the membrane potential

C_m

pF

Capacity of the membrane

tau_m

ms

Membrane time constant

t_ref

ms

Duration of refractory period

V_th

mV

Spike threshold

V_reset

mV

Reset potential of the membrane

tau_syn_ex

ms

Rise time of the excitatory synaptic function

tau_syn_in

ms

Rise time of the inhibitory synaptic function

I_e

pA

Constant external input current

References

Sends

SpikeEvent

Receives

SpikeEvent, CurrentEvent, DataLoggingRequest

See also

Neuron, Integrate-And-Fire, Current-Based, Precise