amat2_psc_exp – Non-resetting leaky integrate-and-fire neuron model with exponential PSCs and adaptive threshold
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Description
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``amat2_psc_exp`` is an implementation of a leaky integrate-and-fire model
with exponential shaped postsynaptic currents (PSCs). Thus, postsynaptic
currents have an infinitely short rise time.
The threshold is lifted when the neuron is fired and then decreases in a
fixed time scale toward a fixed level [3]_.
The threshold crossing is followed by a total refractory period
during which the neuron is not allowed to fire, even if the membrane
potential exceeds the threshold. The membrane potential is NOT reset,
but continuously integrated.
The linear subthreshold dynamics is integrated by the Exact
Integration scheme [1]_. The neuron dynamics is solved on the time
grid given by the computation step size. Incoming as well as emitted
spikes are forced to that grid.
An additional state variable and the corresponding differential
equation represents a piecewise constant external current.
The general framework for the consistent formulation of systems with
neuron like dynamics interacting by point events is described in
[1]_. A flow chart can be found in [2]_.
The default parameter values for this model are different from the
corresponding parameter values for ``mat2_psc_exp``. If identical
parameters are used, and beta is 0, then this model shall behave
exactly as mat2_psc_exp.
The following state variables can be read out using a multimeter:
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V_m mV Non-resetting membrane potential
V_th mV Two-timescale adaptive threshold
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See also [4]_.
Parameters
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The following parameters can be set in the status dictionary:
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C_m pF Capacity of the membrane
E_L mV Resting potential
tau_m ms Membrane time constant
tau_syn_ex ms Time constant of postsynaptic excitatory currents
tau_syn_in ms Time constant of postsynaptic inhibitory currents
t_ref ms Duration of absolute refractory period (no spiking)
V_m mV Membrane potential
I_e pA Constant input current
t_spike ms Point in time of last spike
tau_1 ms Short time constant of adaptive threshold [3, eqs 2-3]
tau_2 ms Long time constant of adaptive threshold [3, eqs 2-3]
alpha_1 mV Amplitude of short time threshold adaption [3, eqs 2-3]
alpha_2 mV Amplitude of long time threshold adaption [3, eqs 2-3]
tau_v ms Time constant of kernel for voltage-dependent threshold
component [3, eqs 16-17]
beta 1/ms Scaling coefficient for voltage-dependent threshold
component [3, eqs 16-17]
omega mV Resting spike threshold (absolute value, not
relative to E_L as in [3]_)
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.. note::
- The time constants in the model must fulfill the following conditions:
- :math:`\tau_m != {\tau_{syn_{ex}}, \tau_{syn_{in}}}`
- :math:`\tau_v != {\tau_{syn_{ex}}, \tau_{syn_{in}}}`
- :math:`\tau_m != \tau_v`
This is required to avoid singularities in the numerics. This is a
problem of implementation only, not a principal problem of the model.
- Expect unstable numerics if time constants that are required to be
different are very close.
- :math:`\tau_m != \tau_{syn_{ex,in}}` is required by the current
implementation to avoid a degenerate case of the ODE describing the
model [1]_. For very similar values, numerics will be unstable.
- Some parameter values given in Table 1 of [4]_ are incorrect. For
correct values, see Table 4 of [5]_.
References
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.. [1] 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
.. [2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001). State
space analysis of synchronous spiking in cortical neural
networks. Neurocomputing 38-40:565-571.
DOI: https://doi.org/10.1016/S0925-2312(01)00409-X
.. [3] Kobayashi R, Tsubo Y and Shinomoto S (2009). Made-to-order
spiking neuron model equipped with a multi-timescale adaptive
threshold. Frontiers in Computational Neuroscience, 3:9.
DOI: https://dx.doi.org/10.3389%2Fneuro.10.009.2009
.. [4] Yamauchi S, Kim H, Shinomoto S (2011). Elemental spiking neuron model
for reproducing diverse firing patterns and predicting precise
firing times. Frontiers in Computational Neuroscience, 5:42.
DOI: https://doi.org/10.3389/fncom.2011.00042
.. [5] Heiberg T, Kriener B, Tetzlaff T, Einevoll GT, Plesser HE (2018).
Firing-rate model for neurons with a broad repertoire of spiking behaviors.
J Comput Neurosci, 45:103.
DOI: https://doi.org/10.1007/s10827-018-0693-9
Sends
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SpikeEvent
Receives
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SpikeEvent, CurrentEvent, DataLoggingRequest
Examples using this model
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.. listexamples:: amat2_psc_exp