traub_psc_alpha

traub_psc_alpha - Traub model according to Borgers 2017

Reduced Traub-Miles Model of a Pyramidal Neuron in Rat Hippocampus [1]. parameters got from reference [2].

Incoming spike events induce a post-synaptic change of current modelled by an alpha function.

References

See also

hh_cond_exp_traub

Parameters

Name

Physical unit

Default value

Description

t_ref

ms

2ms

Refractory period

g_Na

nS

10000nS

Sodium peak conductance

g_K

nS

8000nS

Potassium peak conductance

g_L

nS

10nS

Leak conductance

C_m

pF

100pF

Membrane capacitance

E_Na

mV

50mV

Sodium reversal potential

E_K

mV

-100mV

Potassium reversal potential

E_L

mV

-67mV

Leak reversal potential (aka resting potential)

V_Tr

mV

-20mV

Spike threshold

tau_syn_exc

ms

0.2ms

Rise time of the excitatory synaptic alpha function

tau_syn_inh

ms

2ms

Rise time of the inhibitory synaptic alpha function

I_e

pA

0pA

constant external input current

State variables

Name

Physical unit

Default value

Description

r

integer

0

number of steps in the current refractory phase

V_m

mV

-70.0mV

Membrane potential

Act_m

real

alpha_m_init / (alpha_m_init + beta_m_init)

Activation variable m for Na

Inact_h

real

alpha_h_init / (alpha_h_init + beta_h_init)

Inactivation variable h for Na

Act_n

real

alpha_n_init / (alpha_n_init + beta_n_init)

Activation variable n for K

Equations

\[\frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-(I_{Na} + I_{K} + I_{L}) + I_{e} + I_{stim} + I_{syn,exc} - I_{syn,inh}) } \right)\]
\[\frac{ dAct_{n} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{n} \cdot (1 - Act_{n}) - \beta_{n} \cdot Act_{n}) } \right)\]
\[\frac{ dAct_{m} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{m} \cdot (1 - Act_{m}) - \beta_{m} \cdot Act_{m}) } \right)\]
\[\frac{ dInact_{h} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{h} \cdot (1 - Inact_{h}) - \beta_{h} \cdot Inact_{h}) } \right)\]

Source code

The model source code can be found in the NESTML models repository here: traub_psc_alpha.

Characterisation