amat2_psc_exp – Non-resetting leaky integrate-and-fire neuron model with exponential PSCs and adaptive threshold

Description

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:

V_m	mV	Non-resetting membrane potential
V_th	mV	Two-timescale adaptive threshold

See also [4].

Parameters

The following parameters can be set in the status dictionary:

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])

Note

• The time constants in the model must fulfill the following conditions: - \(\tau_m != {\tau_{syn_{ex}}, \tau_{syn_{in}}}\) - \(\tau_v != {\tau_{syn_{ex}}, \tau_{syn_{in}}}\) - \(\tau_m != \tau_v\) 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.

• \(\tau_m != \tau_{syn_{ex,in}}\) 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

[1] (1,2,3)

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] (1,2)

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] (1,2)

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

SpikeEvent

Receives

SpikeEvent, CurrentEvent, DataLoggingRequest

Examples using this model

None