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.