From 60daaf3b7851e7ba6f4cc3c146cdcd1ba279c806 Mon Sep 17 00:00:00 2001 From: Pedro Mendes Date: Fri, 20 Sep 2024 09:55:12 -0400 Subject: [PATCH] deleted --- examples/Neuron_networks_I/GenericNeuron.cps | 1421 ------------------ 1 file changed, 1421 deletions(-) delete mode 100644 examples/Neuron_networks_I/GenericNeuron.cps diff --git a/examples/Neuron_networks_I/GenericNeuron.cps b/examples/Neuron_networks_I/GenericNeuron.cps deleted file mode 100644 index c44f31a..0000000 --- a/examples/Neuron_networks_I/GenericNeuron.cps +++ /dev/null @@ -1,1421 +0,0 @@ - - - - - - - - - - - Pospischil et al. (2008) Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons. Biological Cybernetics 99:427–441 - - - - - - Giannari and Astolfi (2022) Model design for networks of heterogeneous Hodgkin–Huxley neurons. Neurocomputing 496:147–157 - - - - - - 2024-07-01T17:15:43Z - - - - - pmendes@uchc.edu - - - Mendes - Pedro - - - - - University of Connecticut School of Medicine - - - - - - - - - - -

Generic neuron model

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This is a 9-ODE generic neuron model, inspired by the Hodgin-Huxley model, that includes three types of complex behaviour:

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  • fast spiking (FS)
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  • regular spiking with adaptation (RSA)
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  • intrinsically bursting (IB)
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This is achieved by incorporating terms and differential equations for slow potassium conductance and calcium conductance (in addition to the leak, sodium and fast potassium conductances of the HH model). The parameter values were determined by Pospischil et al. (2008) and re-used in Giannari and Astolfi (2022). This model follows the symbols used in Giannari and Astolfi, but note that the equation for beta_m in that paper is wrong (Pospischil et al. contain the correct version).

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In order to model each type of neuron (FS, RSA or IB), various parameter values need to be adjusted, including setting some maximal conductances to zero (g_Ca and g_M), which make the voltage (V) independent from some of the ion channels (calcium and slow potassium). For convenience, there is a parameter set stored for each neuron type. The parameter scan feature can be used to compare each neuron type with the same perturbations on injected current.

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Since this model is based on voltages and intensities, all variables and differential equations are defined under Global Quantities (there are no reactions and no species here).

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References:
-- Pospischil M, Toledo-Rodriguez M, Monier C, Piwkowska Z, Bal T, Frégnac Y, Markram H, Destexhe A (2008) Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons. Biological Cybernetics 99:427–441
-- Giannari AG, Astolfi A (2022) Model design for networks of heterogeneous Hodgkin–Huxley neurons. Neurocomputing 496:147–157 -

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CC0 1.0 Universal: To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. Please refer to CC0 Public Domain Dedication for more information.

- - - - - - - - - - - - mV - - - - - - - - - - - mV - - - - - - - - - - - mV - - - - - - - - - - - mV - - - - - - - - - - - mV - - - - - - - - - - - uF/cm^2 - - - - - - - - - - - mS/cm^2 - - - - - - - - - - - mS/cm^2 - - - - - - - - - - - mS/cm^2 - - - - - - - - - - - mS/cm^2 - - - - - - - - - - - mS/cm^2 - - - - - - - - - - - ms - - - - - - - - - - - uA/cm^2 - - - - - - - - - - - (<CN=Root,Model=Generic neuron model,Vector=Values[I_inj],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[g_K],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[n],Reference=Value>^4*(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_K],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[g_Na],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[m],Reference=Value>^3*<CN=Root,Model=Generic neuron model,Vector=Values[h],Reference=Value>*(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_Na],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[g_L],Reference=Value>*(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_L],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[g_M],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[p],Reference=Value>*(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_K],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[g_Ca],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[q],Reference=Value>^2*<CN=Root,Model=Generic neuron model,Vector=Values[s],Reference=Value>*(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_Ca],Reference=Value>))/<CN=Root,Model=Generic neuron model,Vector=Values[C_M],Reference=Value> - - - mV - - - - - (<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-15)*(-0.032/(exp(-(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-15)/5)-1)) - - - - - (<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-13)*(-0.32/(exp(-(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-13)/4)-1)) - - - - - 0.128*exp(-(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-17)/18) - - - - - 1/(exp(-(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>+35)/10)+1) - - - 1 - - - - - - - - - - - 0.0055*(-27-<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>)/(exp((-27-<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>)/3.8)-1) - - - - - - - - - - - 0.000457*exp((-13-<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>)/50) - - - - - - - - - - - 0.5*exp(-(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-10)/40) - - - - - This equation is wrong on Giannari et al. but is correctly shown on Pospischil et al. (In Giannari they missed the denominator) - - - 0.28*(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-40)/(exp((<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-40)/5)-1) - - - - - - - - - - - 4/(exp(-(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[V_T],Reference=Value>-40)/5)+1) - - - - - - - - - - - 0.0065/(exp((-15-<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>)/28)+1) - - - - - - - - - - - 0.94*exp((-75-<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>)/17) - - - - - <CN=Root,Model=Generic neuron model,Vector=Values[alpha_n],Reference=Value>*(1-<CN=Root,Model=Generic neuron model,Vector=Values[n],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[beta_n],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[n],Reference=Value> - - - 1 - - - - - <CN=Root,Model=Generic neuron model,Vector=Values[alpha_m],Reference=Value>*(1-<CN=Root,Model=Generic neuron model,Vector=Values[m],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[beta_m],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[m],Reference=Value> - - - 1 - - - - - <CN=Root,Model=Generic neuron model,Vector=Values[alpha_h],Reference=Value>*(1-<CN=Root,Model=Generic neuron model,Vector=Values[h],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[beta_h],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[h],Reference=Value> - - - 1 - - - - - (<CN=Root,Model=Generic neuron model,Vector=Values[p_inf],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[p],Reference=Value>)/<CN=Root,Model=Generic neuron model,Vector=Values[tau_p],Reference=Value> - - - 1 - - - - - <CN=Root,Model=Generic neuron model,Vector=Values[alpha_q],Reference=Value>*(1-<CN=Root,Model=Generic neuron model,Vector=Values[q],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[beta_q],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[q],Reference=Value> - - - 1 - - - - - <CN=Root,Model=Generic neuron model,Vector=Values[alpha_s],Reference=Value>*(1-<CN=Root,Model=Generic neuron model,Vector=Values[s],Reference=Value>)-<CN=Root,Model=Generic neuron model,Vector=Values[beta_s],Reference=Value>*<CN=Root,Model=Generic neuron model,Vector=Values[s],Reference=Value> - - - 1 - - - - - <CN=Root,Model=Generic neuron model,Vector=Values[tau_max],Reference=Value>/(3.3*exp((<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>+35)/20)+exp(-(<CN=Root,Model=Generic neuron model,Vector=Values[V],Reference=Value>+35)/20)) - - - ms - - - - - - - - - - - ms - - - - - - - - - - - ms - - - - - - - - - - - ms - - - - - uA/cm^2 - - - - - - - - - - - ms - - - - - - - - - - - - - <CN=Root,Model=Generic neuron model,Reference=Time> > <CN=Root,Model=Generic neuron model,Vector=Values[pulse_start],Reference=Value> - - - - - uniform(0,1)*<CN=Root,Model=Generic neuron model,Vector=Values[pulse_intensity],Reference=Value> - - - - - if(<CN=Root,Model=Generic neuron model,Reference=Time> < <CN=Root,Model=Generic neuron model,Vector=Values[pulse_end],Reference=Value>-<CN=Root,Model=Generic neuron model,Vector=Values[pulse_length],Reference=Value>,<CN=Root,Model=Generic neuron model,Reference=Time>+<CN=Root,Model=Generic neuron model,Vector=Values[pulse_length],Reference=Value>,0) - - - - - - - - - - - - - <CN=Root,Model=Generic neuron model,Reference=Time> > <CN=Root,Model=Generic neuron model,Vector=Values[pulse_end],Reference=Value> - - - - - <CN=Root,Model=Generic neuron model,Vector=Values[I_inj],Reference=InitialValue> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 0 -71.422484777388902 0.0024408346602080356 0.00050396780896343749 0.9999166710832823 0.025524801576272835 2.687052097323317e-06 0.65786754803808833 0.0022983668646933729 0.0077965530019377272 0.76675575380063665 0.025524801576272842 2.0464977745800642e-06 0.0014701696575933458 0.93933315867971978 15.462542748973913 6.3898250923269661e-05 0.00076458057743151911 0.76161242931334339 90.57208370923459 -90 50 -70 120 -56.200000000000003 1 5 0.029999999999999999 0.20000000000000001 50 0.01 608 0 500 2000 0 1 30 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Automatically generated report. - -