iaf_psc_exp_ps_lossless – Current-based leaky integrate-and-fire neuron with exponential-shaped postsynaptic currents predicting the exact number of spikes using a state space analysis¶
Description¶
iaf_psc_exp_ps_lossless is the precise state space implementation of the leaky
integrate-and-fire model neuron with exponential postsynaptic currents
that uses time reversal to detect spikes [1]. This is the most exact
implementation available.
Time-reversed state space analysis provides a general method to solve the threshold-detection problem for an integrable, affine or linear time evolution. This method is based on the idea of propagating the threshold backwards in time, and see whether it meets the initial state, rather than propagating the initial state forward in time and see whether it meets the threshold.
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 Integration Singularity notebook.
Parameters¶
The following parameters can be set in the status dictionary.
E_L |
mV |
Resting membrane potential |
C_m |
pF/mum^2 |
Specific capacitance of the membrane |
tau_m |
ms |
Membrane time constant |
tau_syn_ex |
ms |
Excitatory synaptic time constant |
tau_syn_in |
ms |
Inhibitory synaptic time constant |
t_ref |
ms |
Duration of refractory period |
V_th |
mV |
Spike threshold |
I_e |
pA |
Constant input current |
V_min |
mV |
Absolute lower value for the membrane potential. |
V_reset |
mV |
Reset value for the membrane potential. |
References¶
Sends¶
SpikeEvent
Receives¶
SpikeEvent, CurrentEvent, DataLoggingRequest
See also¶
iaf_psc_exp_ps