Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

glif_cond – Conductance-based generalized leaky integrate and fire (GLIF) model

Description

glif_cond provides five generalized leaky integrate and fire (GLIF) models 1 with conductance-based synapses. Incoming spike events induce a postsynaptic change of conductance modeled by an alpha function 2. The alpha function is normalized such that an event of weight 1.0 results in a peak conductance change of 1 nS at t = tau_syn. On the postsynaptic side, there can be arbitrarily many synaptic time constants. This can be reached by specifying separate receptor ports, each for a different time constant. The port number has to match the respective “receptor_type” in the connectors.

The five GLIF models are:

  • GLIF Model 1 - Traditional leaky integrate and fire (LIF)

  • GLIF Model 2 - Leaky integrate and fire with biologically defined reset rules (LIF_R)

  • GLIF Model 3 - Leaky integrate and fire with after-spike currents (LIF_ASC)

  • GLIF Model 4 - Leaky integrate and fire with biologically defined reset rules and after-spike currents (LIF_R_ASC)

  • GLIF Model 5 - Leaky integrate and fire with biologically defined reset rules, after-spike currents and a voltage dependent threshold (LIF_R_ASC_A)

Remarks:

GLIF model mechanism setting is based on three parameters (spike_dependent_threshold, after_spike_currents, adapting_threshold). The settings of these three parameters for the five GLIF models are listed below. Other combinations of these parameters will not be supported.

Parameter settings

GLIF Model 1

False

False

False

GLIF Model 2

True

False

False

GLIF Model 3

False

True

False

GLIF Model 4

True

True

False

GLIF Model 5

True

True

True

Typical parameter setting of different levels of GLIF models for different cells can be found and downloaded in the Allen Cell Type Database. For example, the default parameter setting of this glif_cond neuron model was from the parameter values of GLIF Model 5 of Cell 490626718, which can be retrieved from the Allen Brain Atlas, with units being converted from SI units (i.e., V, S (1/Ohm), F, s, A) to NEST used units (i.e., mV, nS (1/GOhm), pF, ms, pA) and values being rounded to appropriate digits for simplification.

For models with spike dependent threshold (i.e., GLIF 2, GLIF 4 and GLIF 5), parameter setting of voltage_reset_fraction and voltage_reset_add may lead to the situation that voltage is bigger than threshold after reset. In this case, the neuron will continue to spike until the end of the simulation regardless the stimulated inputs. We recommend the setting of the parameters of these three models to follow the condition of (E_L + voltage_reset_fraction * ( V_th - E_L ) + voltage_reset_add) < (V_th + th_spike_add).

Parameters

The following parameters can be set in the status dictionary.

Membrane parameters

V_m

double

Membrane potential in mV (absolute value)

V_th

double

Instantaneous threshold in mV

g

double

Membrane conductance in nS

E_L

double

Resting membrane potential in mV

C_m

double

Capacitance of the membrane in pF

t_ref

double

Duration of refractory time in ms

V_reset

double

Reset potential of the membrane in mV (GLIF 1 or GLIF 3)

Spike adaptation and firing intensity parameters

th_spike_add

double

Threshold addition following spike in mV (delta_theta_s in Equation (6) in [1])

th_spike_decay

double

Spike-induced threshold time constant in 1/ms (bs in Equation (2) in [1])

voltage_reset_fraction

double

Voltage fraction coefficient following spike (fv in Equation (5) in [1])

voltage_reset_add

double

Voltage addition following spike in mV (-delta_V (sign flipped) in Equation (5) in [1])

asc_init

double vector

Initial values of after-spike currents in pA

asc_decay

double vector

After-spike current time constants in 1/ms (kj in Equation (3) in [1])

asc_amps

double vector

After-spike current amplitudes in pA (deltaIj in Equation (7) in [1])

asc_r

double vector

Current fraction following spike coefficients for fj in Equation (7) in [1]

th_voltage_index

double

Adaptation index of threshold - A ‘leak-conductance’ for the voltage-dependent component of the threshold in 1/ms (av in Equation (4) in [1])

th_voltage_decay

double

Voltage-induced threshold time constant - Inverse of which is the time constant of the voltage-dependent component of the threshold in 1/ms (bv in Equation (4) in [1])

tau_syn

double vector

Rise time constants of the synaptic alpha function in ms

E_rev

double vector

Reversal potential in mV

spike_dependent_threshold

bool

flag whether the neuron has biologically defined reset rules with a spike dependent threshold component

after_spike_currents

bool

flag whether the neuron has after spike currents

adapting_threshold

bool

flag whether the neuron has a voltage dependent threshold component

References

1

Teeter C, Iyer R, Menon V, Gouwens N, Feng D, Berg J, Szafer A, Cain N, Zeng H, Hawrylycz M, Koch C, & Mihalas S (2018) Generalized leaky integrate-and-fire models classify multiple neuron types. Nature Communications 9:709.

2

Meffin, H., Burkitt, A. N., & Grayden, D. B. (2004). An analytical model for the large, fluctuating synaptic conductance state typical of neocortical neurons in vivo. J. Comput. Neurosci., 16, 159-175.