iaf_psc_exp_ps_lossless - Leaky integrate-and-fire neuron
with exponential postsynaptic currents; precise implementation;
predicts exact number of spikes by applying state space analysi
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.
The following parameters can be set in the status dictionary.
E_L double - Resting membrane potential in mV.
C_m double - Specific capacitance of the membrane in pF/mum^2.
tau_m double - Membrane time constant in ms.
tau_syn_ex double - Excitatory synaptic time constant in ms.
tau_syn_in double - Inhibitory synaptic time constant in ms.
t_ref double - Duration of refractory period in ms.
V_th double - Spike threshold in mV.
I_e double - Constant input current in pA.
V_min double - Absolute lower value for the membrane potential.
V_reset double - Reset value for the membrane potential.
SpikeEvent, CurrentEvent, DataLoggingRequest
SpikeEvent
This model transmits precise spike times to target nodes (on-grid spike
time and offset). If this node is connected to a spike_detector, the
property "precise_times" of the spike_detector has to be set to true in
order to record the offsets in addition to the on-grid spike times.
The iaf_psc_delta_ps neuron accepts connections transmitting
CurrentEvents. These events transmit stepwise-constant currents which
can only change at on-grid times.
In the current implementation, tau_syn_ex and tau_syn_in must be equal.
This is because the state space would be 3-dimensional otherwise, which
makes the detection of threshold crossing more difficult [1].
Support for different time constants may be added in the future,
see issue #921.
[1] Krishnan J, Porta Mana P, Helias M, Diesmann M and Di Napoli E
(2018) Perfect Detection of Spikes in the Linear Sub-threshold
Dynamics of Point Neurons. Front. Neuroinform. 11:75.
doi: 10.3389/fninf.2017.00075
Jeyashree Krishnan
/var/www/debian/nest/nest-simulator-2.18.0/precise/iaf_psc_exp_ps_lossless.h