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Merge pull request #343 from PTraeder/model-review
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Make an initial review of NESTML neuron models with regard to the used physical units in the model specification.
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Plotnikov authored Apr 10, 2017
2 parents 1374b77 + c0a589c commit 279c571
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Showing 46 changed files with 547 additions and 400 deletions.
2 changes: 1 addition & 1 deletion models/aeif_cond_alpha.nestml
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Expand Up @@ -119,7 +119,7 @@ neuron aeif_cond_alpha_neuron:
emit_spike()
end

I_stim = currents.get_sum()*1pA
I_stim = currents
end

end
10 changes: 5 additions & 5 deletions models/aeif_cond_alpha_implicit.nestml
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Expand Up @@ -42,11 +42,11 @@ neuron aeif_cond_alpha_implicit:
equations:
function V_bounded mV = min(V_m, V_peak) # prevent exponential divergence
# alpha function for the g_in
g_in'' = (-2/tau_syn_in) * g_in'-(1/tau_syn_in**2) * g_in
g_in'' = (-2/tau_syn_in) * g_in'-(1/tau_syn_in**2) * g_in
g_in' = g_in'

# alpha function for the g_ex
g_ex'' = (-2/tau_syn_ex) * g_ex'-(1/tau_syn_ex**2) * g_ex
g_ex'' = (-2/tau_syn_ex) * g_ex'-(1/tau_syn_ex**2) * g_ex
g_ex' = g_ex'

# Add aliases to simplify the equation definition of V_m
Expand Down Expand Up @@ -125,9 +125,9 @@ neuron aeif_cond_alpha_implicit:
emit_spike()
end

g_ex' += spikesExc.get_sum() * PSConInit_E
g_in' += spikesInh.get_sum() * PSConInit_I
I_stim = currents.get_sum()
g_ex' += spikesExc * PSConInit_E
g_in' += spikesInh * PSConInit_I
I_stim = currents
end

end
2 changes: 1 addition & 1 deletion models/aeif_cond_exp.nestml
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Expand Up @@ -112,7 +112,7 @@ neuron aeif_cond_exp_neuron:
emit_spike()
end

I_stim = currents.get_sum()
I_stim = currents
end

end
6 changes: 3 additions & 3 deletions models/aeif_cond_exp_implicit.nestml
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Expand Up @@ -115,9 +115,9 @@ neuron aeif_cond_exp_implicit:
emit_spike()
end

g_ex += spikeExc.get_sum()
g_in += spikeInh.get_sum()
I_stim = currents.get_sum()
g_ex += spikeExc * 1 nS
g_in += spikeInh * 1 nS
I_stim = currents
end

end
32 changes: 16 additions & 16 deletions models/hh_cond_exp_traub.nestml
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Expand Up @@ -37,12 +37,12 @@ neuron hh_cond_exp_traub_neuron:
V_m mV = E_L # Membrane potential

# equilibrium values for (in)activation variables
function alpha_n_init real = 0.032 * ( 15. - V_m ) / ( exp( ( 15. - V_m ) / 5. ) - 1. )
function beta_n_init real = 0.5 * exp( ( 10. - V_m ) / 40. )
function alpha_m_init real = 0.32 * ( 13. - V_m ) / ( exp( ( 13. - V_m ) / 4. ) - 1. )
function beta_m_init real = 0.28 * ( V_m - 40. ) / ( exp( ( V_m - 40. ) / 5. ) - 1. )
function alpha_h_init real = 0.128 * exp( ( 17. - V_m ) / 18. )
function beta_h_init real = 4. / ( 1. + exp( ( 40. - V_m ) / 5. ) )
function alpha_n_init 1/ms = 0.032/(1 ms* 1 mV ) * ( 15. mV - V_m) / ( exp( ( 15. mV - V_m) / 5. mV ) - 1. )
function beta_n_init 1/ms = 0.5 /1 ms * exp( ( 10. mV - V_m ) / 40. mV )
function alpha_m_init 1/ms = 0.32/(1 ms* 1 mV ) * ( 13. mV - V_m) / ( exp( ( 13. mV - V_m) / 4. mV ) - 1. )
function beta_m_init 1/ms = 0.28/(1 ms* 1 mV ) * ( V_m - 40. mV ) / ( exp( ( V_m - 40. mV ) / 5. mV ) - 1. )
function alpha_h_init 1/ms = 0.128/1 ms * exp( ( 17. mV - V_m) / 18. mV )
function beta_h_init 1/ms = ( 4. / ( 1. + exp( ( 40. mV - V_m ) / 5. mV) ) ) / 1 ms

Act_m real = alpha_m_init / ( alpha_m_init + beta_m_init )
Act_h real = alpha_h_init / ( alpha_h_init + beta_h_init )
Expand All @@ -67,16 +67,16 @@ neuron hh_cond_exp_traub_neuron:

# channel dynamics
function V_rel mV = V_m - V_T
function alpha_n real = 0.032 * ( 15. - V_rel ) / ( exp( ( 15. - V_rel ) / 5. ) - 1. )
function beta_n real = 0.5 * exp( ( 10. - V_rel ) / 40. )
function alpha_m real = 0.32 * ( 13. - V_rel ) / ( exp( ( 13. - V_rel ) / 4. ) - 1. )
function beta_m real = 0.28 * ( V_rel - 40. ) / ( exp( ( V_rel - 40. ) / 5. ) - 1. )
function alpha_h real = 0.128 * exp( ( 17. - V_rel ) / 18. )
function beta_h real = 4. / ( 1. + exp( ( 40. - V_rel ) / 5. ) )
function alpha_n 1/ms = 0.032/(1 ms* 1 mV ) * ( 15. mV - V_m) / ( exp( ( 15. mV - V_m) / 5. mV ) - 1. )
function beta_n 1/ms = 0.5 /1 ms * exp( ( 10. mV - V_m ) / 40. mV )
function alpha_m 1/ms = 0.32/(1 ms* 1 mV ) * ( 13. mV - V_m) / ( exp( ( 13. mV - V_m) / 4. mV ) - 1. )
function beta_m 1/ms = 0.28/(1 ms* 1 mV ) * ( V_m - 40. mV ) / ( exp( ( V_m - 40. mV ) / 5. mV ) - 1. )
function alpha_h 1/ms = 0.128/1 ms * exp( ( 17. mV - V_m) / 18. mV )
function beta_h 1/ms = ( 4. / ( 1. + exp( ( 40. mV - V_m ) / 5. mV) ) ) / 1 ms

Act_m' = alpha_m - ( alpha_m + beta_m ) * Act_m
Act_h' = alpha_h - ( alpha_h + beta_h ) * Act_h
Inact_n' = alpha_n - ( alpha_n + beta_n ) * Inact_n
Act_m' = ( alpha_m - ( alpha_m + beta_m ) * Act_m )
Act_h' = ( alpha_h - ( alpha_h + beta_h ) * Act_h )
Inact_n' = ( alpha_n - ( alpha_n + beta_n ) * Inact_n )

end

Expand Down Expand Up @@ -124,7 +124,7 @@ neuron hh_cond_exp_traub_neuron:
end

# set new input current
I_stim = currents.get_sum()
I_stim = currents

end

Expand Down
36 changes: 18 additions & 18 deletions models/hh_cond_exp_traub_implicit.nestml
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Expand Up @@ -40,12 +40,12 @@ neuron hh_cond_exp_traub_implicit:
g_ex nS = 0nS # Excitatory synaptic conductance

# TODO: it should be possible, to define these variables in the internal block
function alpha_n_init real = 0.032 * ( 15. - V_m ) / ( exp( ( 15. - V_m ) / 5. ) - 1. )
function beta_n_init real = 0.5 * exp( ( 10. - V_m ) / 40. )
function alpha_m_init real = 0.32 * ( 13. - V_m ) / ( exp( ( 13. - V_m ) / 4. ) - 1. )
function beta_m_init real = 0.28 * ( V_m - 40. ) / ( exp( ( V_m - 40. ) / 5. ) - 1. )
function alpha_h_init real = 0.128 * exp( ( 17. - V_m ) / 18. )
function beta_h_init real = 4. / ( 1. + exp( ( 40. - V_m ) / 5. ) )
function alpha_n_init 1/ms = 0.032/(1 ms* 1 mV ) * ( 15. mV - V_m) / ( exp( ( 15. mV - V_m) / 5. mV ) - 1. )
function beta_n_init 1/ms = 0.5 /1 ms * exp( ( 10. mV - V_m ) / 40. mV )
function alpha_m_init 1/ms = 0.32/(1 ms* 1 mV ) * ( 13. mV - V_m) / ( exp( ( 13. mV - V_m) / 4. mV ) - 1. )
function beta_m_init 1/ms = 0.28/(1 ms* 1 mV ) * ( V_m - 40. mV ) / ( exp( ( V_m - 40. mV ) / 5. mV ) - 1. )
function alpha_h_init 1/ms = 0.128/1 ms * exp( ( 17. mV - V_m) / 18. mV )
function beta_h_init 1/ms = ( 4. / ( 1. + exp( ( 40. mV - V_m ) / 5. mV) ) ) / 1 ms

Act_m real = alpha_m_init / ( alpha_m_init + beta_m_init )
Act_h real = alpha_h_init / ( alpha_h_init + beta_h_init )
Expand All @@ -64,16 +64,16 @@ neuron hh_cond_exp_traub_implicit:

# equilibrium values for (in)activation variables
function V_rel mV = V_m - V_T
function alpha_n real = 0.032 * ( 15. - V_rel ) / ( exp( ( 15. - V_rel ) / 5. ) - 1. )
function beta_n real = 0.5 * exp( ( 10. - V_rel ) / 40. )
function alpha_m real = 0.32 * ( 13. - V_rel ) / ( exp( ( 13. - V_rel ) / 4. ) - 1. )
function beta_m real = 0.28 * ( V_rel - 40. ) / ( exp( ( V_rel - 40. ) / 5. ) - 1. )
function alpha_h real = 0.128 * exp( ( 17. - V_rel ) / 18. )
function beta_h real = 4. / ( 1. + exp( ( 40. - V_rel ) / 5. ) )
function alpha_n 1/ms = 0.032/(1 ms* 1 mV ) * ( 15. mV - V_m) / ( exp( ( 15. mV - V_m) / 5. mV ) - 1. )
function beta_n 1/ms = 0.5 /1 ms * exp( ( 10. mV - V_m ) / 40. mV )
function alpha_m 1/ms = 0.32/(1 ms* 1 mV ) * ( 13. mV - V_m) / ( exp( ( 13. mV - V_m) / 4. mV ) - 1. )
function beta_m 1/ms = 0.28/(1 ms* 1 mV ) * ( V_m - 40. mV ) / ( exp( ( V_m - 40. mV ) / 5. mV ) - 1. )
function alpha_h 1/ms = 0.128/1 ms * exp( ( 17. mV - V_m) / 18. mV )
function beta_h 1/ms = ( 4. / ( 1. + exp( ( 40. mV - V_m ) / 5. mV) ) ) / 1 ms

Act_m' = alpha_m - ( alpha_m + beta_m ) * Act_m
Act_h' = alpha_h - ( alpha_h + beta_h ) * Act_h
Inact_n' = alpha_n - ( alpha_n + beta_n ) * Inact_n
Act_m' = ( alpha_m - ( alpha_m + beta_m ) * Act_m )
Act_h' = ( alpha_h - ( alpha_h + beta_h ) * Act_h )
Inact_n' = ( alpha_n - ( alpha_n + beta_n ) * Inact_n )

# synapses: exponential conductance
g_ex' = -g_ex / tau_syn_ex
Expand Down Expand Up @@ -124,10 +124,10 @@ neuron hh_cond_exp_traub_implicit:
end

# set new input current
I_stim = currents.get_sum()
I_stim = currents

g_ex += spikeExc.get_sum()
g_in += spikeInh.get_sum()
g_ex += spikeExc * 1 nS
g_in += spikeInh * 1 nS
end

end
30 changes: 15 additions & 15 deletions models/hh_psc_alpha.nestml
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Expand Up @@ -49,12 +49,12 @@ neuron hh_psc_alpha_neuron:
state:
V_m mV = -65. mV # Membrane potential

function alpha_n_init real = ( 0.01 * ( V_m + 55. ) ) / ( 1. - exp( -( V_m + 55. ) / 10. ) )
function beta_n_init real = 0.125 * exp( -( V_m + 65. ) / 80. )
function alpha_m_init real = ( 0.1 * ( V_m + 40. ) ) / ( 1. - exp( -( V_m + 40. ) / 10. ) )
function beta_m_init real = 4. * exp( -( V_m + 65. ) / 18. )
function alpha_h_init real = 0.07 * exp( -( V_m + 65. ) / 20. )
function beta_h_init real = 1. / ( 1. + exp( -( V_m + 35. ) / 10. ) )
function alpha_n_init real = ( 0.01 * ( V_m / 1 mV + 55. ) ) / ( 1. - exp( -( V_m / 1 mV + 55. ) / 10. ) )
function beta_n_init real = 0.125 * exp( -( V_m / 1 mV + 65. ) / 80. )
function alpha_m_init real = ( 0.1 * ( V_m / 1 mV + 40. ) ) / ( 1. - exp( -( V_m / 1 mV + 40. ) / 10. ) )
function beta_m_init real = 4. * exp( -( V_m / 1 mV + 65. ) / 18. )
function alpha_h_init real = 0.07 * exp( -( V_m / 1 mV + 65. ) / 20. )
function beta_h_init real = 1. / ( 1. + exp( -( V_m / 1 mV + 35. ) / 10. ) )

Act_m real = alpha_m_init / ( alpha_m_init + beta_m_init ) # Activation variable m
Act_h real = alpha_h_init / ( alpha_h_init + beta_h_init ) # Activation variable h
Expand All @@ -74,19 +74,19 @@ neuron hh_psc_alpha_neuron:
V_m' =( -( I_Na + I_K + I_L ) + currents + I_e + I_syn_inh + I_syn_exc ) / C_m

# Inact_n
function alpha_n real = ( 0.01 * ( V_m + 55. ) ) / ( 1. - exp( -( V_m + 55. ) / 10. ) )
function beta_n real = 0.125 * exp( -( V_m + 65. ) / 80. )
Inact_n' = alpha_n * ( 1 - Inact_n ) - beta_n * Inact_n # n-variable
function alpha_n real = ( 0.01 * ( V_m / 1 mV + 55. ) ) / ( 1. - exp( -( V_m / 1 mV + 55. ) / 10. ) )
function beta_n real = 0.125 * exp( -( V_m / 1 mV + 65. ) / 80. )
Inact_n' = ( alpha_n * ( 1 - Inact_n ) - beta_n * Inact_n ) / 1 ms # n-variable

# Act_m
function alpha_m real = ( 0.1 * ( V_m + 40. ) ) / ( 1. - exp( -( V_m + 40. ) / 10. ) )
function beta_m real = 4. * exp( -( V_m + 65. ) / 18. )
Act_m' = alpha_m * ( 1 - Act_m ) - beta_m * Act_m # m-variable
function alpha_m real = ( 0.1 * ( V_m / 1 mV + 40. ) ) / ( 1. - exp( -( V_m / 1 mV + 40. ) / 10. ) )
function beta_m real = 4. * exp( -( V_m / 1 mV + 65. ) / 18. )
Act_m' = ( alpha_m * ( 1 - Act_m ) - beta_m * Act_m ) / 1 ms # m-variable

# Act_h
function alpha_h real = 0.07 * exp( -( V_m + 65. ) / 20. )
function beta_h real = 1. / ( 1. + exp( -( V_m + 35. ) / 10. ) )
Act_h' = alpha_h * ( 1 - Act_h ) - beta_h * Act_h # h-variable
function alpha_h real = 0.07 * exp( -( V_m / 1 mV + 65. ) / 20. )
function beta_h real = 1. / ( 1. + exp( -( V_m / 1 mV + 35. ) / 10. ) )
Act_h' = ( alpha_h * ( 1 - Act_h ) - beta_h * Act_h ) / 1 ms # h-variable
end

parameters:
Expand Down
36 changes: 18 additions & 18 deletions models/hh_psc_alpha_implicit.nestml
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Expand Up @@ -51,12 +51,12 @@ neuron hh_psc_alpha_implicit:
I_syn_ex pA # inputs from the exc spikes
I_syn_in pA # inputs from the inh spikes

function alpha_n_init real = ( 0.01 * ( V_m + 55. ) ) / ( 1. - exp( -( V_m + 55. ) / 10. ) )
function beta_n_init real = 0.125 * exp( -( V_m + 65. ) / 80. )
function alpha_m_init real = ( 0.1 * ( V_m + 40. ) ) / ( 1. - exp( -( V_m + 40. ) / 10. ) )
function beta_m_init real = 4. * exp( -( V_m + 65. ) / 18. )
function alpha_h_init real = 0.07 * exp( -( V_m + 65. ) / 20. )
function beta_h_init real = 1. / ( 1. + exp( -( V_m + 35. ) / 10. ) )
function alpha_n_init real = ( 0.01 * ( V_m / 1 mV + 55. ) ) / ( 1. - exp( -( V_m / 1 mV + 55. ) / 10. ) )
function beta_n_init real = 0.125 * exp( -( V_m / 1 mV + 65. ) / 80. )
function alpha_m_init real = ( 0.1 * ( V_m / 1 mV + 40. ) ) / ( 1. - exp( -( V_m / 1 mV + 40. ) / 10. ) )
function beta_m_init real = 4. * exp( -( V_m / 1 mV + 65. ) / 18. )
function alpha_h_init real = 0.07 * exp( -( V_m / 1 mV + 65. ) / 20. )
function beta_h_init real = 1. / ( 1. + exp( -( V_m / 1 mV + 35. ) / 10. ) )

Act_m real = alpha_m_init / ( alpha_m_init + beta_m_init ) # Activation variable m
Act_h real = alpha_h_init / ( alpha_h_init + beta_h_init ) # Activation variable h
Expand All @@ -79,19 +79,19 @@ neuron hh_psc_alpha_implicit:
V_m' =( -( I_Na + I_K + I_L ) + I_stim + I_e + I_syn_inh + I_syn_exc ) / C_m

# Inact_n
function alpha_n real = ( 0.01 * ( V_m + 55. ) ) / ( 1. - exp( -( V_m + 55. ) / 10. ) )
function beta_n real = 0.125 * exp( -( V_m + 65. ) / 80. )
Inact_n' = alpha_n * ( 1 - Inact_n ) - beta_n * Inact_n # n-variable
function alpha_n real = ( 0.01 * ( V_m / 1 mV + 55. ) ) / ( 1. - exp( -( V_m / 1 mV + 55. ) / 10. ) )
function beta_n real = 0.125 * exp( -( V_m / 1 mV + 65. ) / 80. )
Inact_n' = ( alpha_n * ( 1 - Inact_n ) - beta_n * Inact_n ) / 1 ms # n-variable

# Act_m
function alpha_m real = ( 0.1 * ( V_m + 40. ) ) / ( 1. - exp( -( V_m + 40. ) / 10. ) )
function beta_m real = 4. * exp( -( V_m + 65. ) / 18. )
Act_m' = alpha_m * ( 1 - Act_m ) - beta_m * Act_m # m-variable
function alpha_m real = ( 0.1 * ( V_m / 1 mV + 40. ) ) / ( 1. - exp( -( V_m / 1 mV + 40. ) / 10. ) )
function beta_m real = 4. * exp( -( V_m / 1 mV + 65. ) / 18. )
Act_m' = ( alpha_m * ( 1 - Act_m ) - beta_m * Act_m ) / 1 ms # m-variable

# Act_h'
function alpha_h real = 0.07 * exp( -( V_m + 65. ) / 20. )
function beta_h real = 1. / ( 1. + exp( -( V_m + 35. ) / 10. ) )
Act_h' = alpha_h * ( 1 - Act_h ) - beta_h * Act_h # h-variable
function alpha_h real = 0.07 * exp( -( V_m / 1 mV + 65. ) / 20. )
function beta_h real = 1. / ( 1. + exp( -( V_m / 1 mV + 35. ) / 10. ) )
Act_h' = ( alpha_h * ( 1 - Act_h ) - beta_h * Act_h ) / 1 ms # h-variable
end

parameters:
Expand Down Expand Up @@ -141,15 +141,15 @@ neuron hh_psc_alpha_implicit:
# sending spikes: crossing 0 mV, pseudo-refractoriness and local maximum...
if r > 0: # is refractory?
r -= 1
elif V_m > 0 and U_old > V_m: # threshold && maximum
elif V_m > 0 mV and U_old > V_m: # threshold && maximum
r = RefractoryCounts
emit_spike()
end

I_stim = currents

I_syn_ex' += spikeExc.get_sum() * PSConInit_E
I_syn_in' += spikeInh.get_sum() * PSConInit_I
I_syn_ex' += spikeExc * PSConInit_E
I_syn_in' += spikeInh * PSConInit_I
end

end
52 changes: 25 additions & 27 deletions models/ht_neuron.nestml
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Expand Up @@ -99,38 +99,36 @@ neuron ht_neuron_nestml:
recordable function I_h pA = -h_g_peak * Ih_m * ( V_m - h_E_rev )
# The spike current is only activate immediately after a spike.
function I_spike mV = (g_spike) ? -( V_m - E_K ) / Tau_spike : 0
V_m' = ( I_Na + I_K + I_syn + I_NaP + I_KNa + I_T + I_h + I_stim ) / Tau_m + I_spike

V_m' = ( ( I_Na + I_K + I_syn + I_NaP + I_KNa + I_T + I_h + I_stim ) / Tau_m + I_spike * 1 [pA/(ms * mV)] ) * 1 [s/nF]
#############
# Intrinsic currents
#############
# I_T
function m_inf_T real = 1.0 / ( 1.0 + exp( -( V_m + 59.0 ) / 6.2 ) )
function h_inf_T real = 1.0 / ( 1.0 + exp( ( V_m + 83.0 ) / 4 ) )
function tau_m_h real = 1.0 / ( exp( -14.59 - 0.086 * V_m ) + exp( -1.87 + 0.0701 * V_m ) )
function m_inf_T real = 1.0 / ( 1.0 + exp( -( V_m / 1 mV + 59.0 ) / 6.2 ) )
function h_inf_T real = 1.0 / ( 1.0 + exp( ( V_m / 1 mV + 83.0 ) / 4 ) )
function tau_m_h real = 1.0 / ( exp( -14.59 - 0.086 * V_m / 1 mV ) + exp( -1.87 + 0.0701 * V_m / 1 mV ) )
# I_KNa
function D_influx_peak real = 0.025
function tau_D real = 1250.0 # yes, 1.25s
function D_thresh real = -10.0
function D_slope real = 5.0
function D_thresh mV = -10.0
function D_slope mV = 5.0
function D_influx real = 1.0 / ( 1.0 + exp( -( V_m - D_thresh ) / D_slope ) )

function I_Na real = -g_NaL * ( V_m - E_Na )
function I_K real = -g_KL * ( V_m - E_K )
function I_Na pA = -g_NaL * ( V_m - E_Na )
function I_K pA = -g_KL * ( V_m - E_K )

Theta' = -( Theta - Theta_eq ) / Tau_theta

# equation modified from y[](1-D_eq) to (y[]-D_eq), since we'd not
# be converging to equilibrium otherwise
IKNa_D' = D_influx_peak * D_influx - ( IKNa_D - KNa_D_EQ ) / tau_D

function tau_m_T real = 0.22 / ( exp( -( V_m + 132.0 ) / 16.7 ) + exp( ( V_m + 16.8 ) / 18.2 ) ) + 0.13
function tau_h_T real = 8.2 + ( 56.6 + 0.27 * exp( ( V_m + 115.2 ) / 5.0 ) ) / ( 1.0 + exp( ( V_m + 86.0 ) / 3.2 ) )
IKNa_D' = ( D_influx_peak * D_influx * 1 nS - ( IKNa_D - KNa_D_EQ * 1 [1/mV] ) / tau_D ) /1 ms
function tau_m_T real = 0.22 / ( exp( -( V_m / 1 mV + 132.0 ) / 16.7 ) + exp( ( V_m / 1 mV + 16.8 ) / 18.2 ) ) + 0.13
function tau_h_T real = 8.2 + ( 56.6 + 0.27 * exp( ( V_m / 1 mV + 115.2 ) / 5.0 ) ) / ( 1.0 + exp( ( V_m / 1 mV + 86.0 ) / 3.2 ) )
function I_h_Vthreshold real = -75.0
function m_inf_h real = 1.0 / ( 1.0 + exp( ( V_m - I_h_Vthreshold ) / 5.5 ) )
IT_m' = ( m_inf_T - IT_m ) / tau_m_T
IT_h' = ( h_inf_T - IT_h ) / tau_h_T
Ih_m' = ( m_inf_h - Ih_m ) / tau_m_h
function m_inf_h real = 1.0 / ( 1.0 + exp( ( V_m / 1 mV - I_h_Vthreshold ) / 5.5 ) )
IT_m' = ( m_inf_T * 1 nS - IT_m ) / tau_m_T /1 ms
IT_h' = ( h_inf_T * 1 nS - IT_h ) / tau_h_T /1 ms
Ih_m' = ( m_inf_h * 1 nS - Ih_m ) / tau_m_h /1 ms

#############
# Synapses
Expand All @@ -139,13 +137,13 @@ neuron ht_neuron_nestml:
g_AMPA' = g_AMPA' - g_AMPA / AMPA_Tau_2

g_NMDA'' = -g_NMDA' / NMDA_Tau_1
g_NMDA' = g_NMDA' - g_NMDA / NMDA_Tau_2
g_NMDA' = g_NMDA' - g_NMDA / NMDA_Tau_2

g_GABAA'' = -g_GABAA' / GABA_A_Tau_1
g_GABAA' = g_GABAA' - g_GABAA / GABA_A_Tau_2
g_GABAA' = g_GABAA' - g_GABAA / GABA_A_Tau_2

g_GABAB'' = -g_GABAB' / GABA_B_Tau_1
g_GABAB' = g_GABAB' - g_GABAB /GABA_B_Tau_2
g_GABAB' = g_GABAB' - g_GABAB /GABA_B_Tau_2
end

parameters:
Expand Down Expand Up @@ -223,10 +221,10 @@ neuron ht_neuron_nestml:
end
r_potassium -= 1

g_AMPA' += AMPAInitialValue * AMPA
g_NMDA' += NMDAInitialValue * NMDA
g_GABAA' += GABA_AInitialValue * GABA_A
g_GABAB' += GABA_BInitialValue * GABA_B
g_AMPA' += AMPAInitialValue * AMPA * 1 [nS/ms]
g_NMDA' += NMDAInitialValue * NMDA * 1 [nS/ms]
g_GABAA' += GABA_AInitialValue * GABA_A * 1 [nS/ms]
g_GABAB' += GABA_BInitialValue * GABA_B * 1 [nS/ms]

if (not g_spike) and V_m >= Theta:
# Set V and Theta to the sodium reversal potential.
Expand All @@ -247,7 +245,7 @@ neuron ht_neuron_nestml:
emit_spike()
end

I_stim = currents.get_sum()
I_stim = currents
end

function compute_synapse_constant(Tau_1 ms, Tau_2 ms, g_peak real) real:
Expand All @@ -256,9 +254,9 @@ neuron ht_neuron_nestml:
# See: Exact digital simulation of time-invariant linear systems
# with applications to neuronal modeling, Rotter and Diesmann,
# section 3.1.2.
exact_integration_adjustment real = ( 1 / Tau_2 ) - ( 1 / Tau_1 )
exact_integration_adjustment real = ( ( 1 / Tau_2 ) - ( 1 / Tau_1 ) ) * 1 ms

t_peak real = ( Tau_2 * Tau_1 ) * log( Tau_2 / Tau_1 ) / ( Tau_2 - Tau_1 )
t_peak real = ( Tau_2 * Tau_1 ) * log( Tau_2 / Tau_1 ) / ( Tau_2 - Tau_1 ) / 1 ms
normalisation_factor real = 1 / ( exp( -t_peak / Tau_1 ) - exp( -t_peak / Tau_2 ) )

return g_peak * normalisation_factor * exact_integration_adjustment
Expand Down
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