automatic model characterisation and doc generator

doc/models_library/aeif_cond_alpha_neuron.rst

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+aeif_cond_alpha_neuron
+######################
+
+
+aeif_cond_alpha - Conductance based exponential integrate-and-fire neuron model
+
+Description
++++++++++++
+
+aeif_cond_alpha is the adaptive exponential integrate and fire neuron according to Brette and Gerstner (2005), with post-synaptic conductances in the form of a bi-exponential ("alpha") function.
+
+The membrane potential is given by the following differential equation:
+
+.. math::
+
+   C_m \frac{dV_m}{dt} =
+   -g_L(V_m-E_L)+g_L\Delta_T\exp\left(\frac{V_m-V_{th}}{\Delta_T}\right) -
+ g_e(t)(V_m-E_e) \\
+                                                     -g_i(t)(V_m-E_i)-w + I_e
+
+and
+
+.. math::
+
+ \tau_w \frac{dw}{dt} = a(V_m-E_L) - w
+
+Note that the membrane potential can diverge to positive infinity due to the exponential term. To avoid numerical instabilities, instead of :math:`V_m`, the value :math:`\min(V_m,V_{peak})` is used in the dynamical equations.
+
+
+References
+++++++++++
+
+.. [1] Brette R and Gerstner W (2005). Adaptive exponential
+       integrate-and-fire model as an effective description of neuronal
+       activity. Journal of Neurophysiology. 943637-3642
+       DOI: https://doi.org/10.1152/jn.00686.2005
+
+
+See also
+++++++++
+
+iaf_cond_alpha, aeif_cond_exp
+
+
+
+Parameters
+++++++++++
+.. csv-table::
+    :header: "Name", "Physical unit", "Default value", "Description"
+    :widths: auto
+
+
+    "C_m", "pF", "281.0pF", "membrane parametersMembrane Capacitance"    
+    "refr_T", "ms", "2ms", "Duration of refractory period"    
+    "V_reset", "mV", "-60.0mV", "Reset Potential"    
+    "g_L", "nS", "30.0nS", "Leak Conductance"    
+    "E_L", "mV", "-70.6mV", "Leak reversal Potential (aka resting potential)"    
+    "a", "nS", "4nS", "spike adaptation parametersSubthreshold adaptation"    
+    "b", "pA", "80.5pA", "Spike-triggered adaptation"    
+    "Delta_T", "mV", "2.0mV", "Slope factor"    
+    "tau_w", "ms", "144.0ms", "Adaptation time constant"    
+    "V_th", "mV", "-50.4mV", "Threshold Potential"    
+    "V_peak", "mV", "0mV", "Spike detection threshold"    
+    "E_exc", "mV", "0mV", "synaptic parametersExcitatory reversal Potential"    
+    "tau_syn_exc", "ms", "0.2ms", "Synaptic Time Constant Excitatory Synapse"    
+    "E_inh", "mV", "-85.0mV", "Inhibitory reversal Potential"    
+    "tau_syn_inh", "ms", "2.0ms", "Synaptic Time Constant for Inhibitory Synapse"    
+    "I_e", "pA", "0pA", "constant external input current"
+
+
+
+State variables
++++++++++++++++
+
+.. csv-table::
+    :header: "Name", "Physical unit", "Default value", "Description"
+    :widths: auto
+
+
+    "V_m", "mV", "E_L", "Membrane potential"    
+    "w", "pA", "0pA", "Spike-adaptation current"    
+    "refr_t", "ms", "0ms", "Refractory period timer"    
+    "is_refractory", "boolean", "false", ""
+
+
+
+
+Equations
++++++++++
+
+
+
+.. math::
+   \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-g_{L} \cdot (V_{bounded} - E_{L}) + I_{spike} - I_{syn,exc} - I_{syn,inh} - w + I_{e} + I_{stim}) } \right) 
+
+.. math::
+   \frac{ dw } { dt }= \frac 1 { \tau_{w} } \left( { (a \cdot (V_{bounded} - E_{L}) - w) } \right) 
+
+
+
+Source code
++++++++++++
+
+The model source code can be found in the NESTML models repository here: `aeif_cond_alpha_neuron <https://github.com/nest/nestml/tree/master/models/neurons/aeif_cond_alpha_neuron.nestml>`_.
+
+Characterisation
+++++++++++++++++
+
+.. include:: aeif_cond_alpha_neuron_characterisation.rst
+
+
+.. footer::
+
+   Generated at 2024-05-14 22:01:11.810891

doc/models_library/aeif_cond_exp_neuron.rst

@@ -0,0 +1,115 @@
+aeif_cond_exp_neuron
+####################
+
+
+aeif_cond_exp - Conductance based exponential integrate-and-fire neuron model
+
+Description
++++++++++++
+
+aeif_cond_exp is the adaptive exponential integrate and fire neuron
+according to Brette and Gerstner (2005), with post-synaptic
+conductances in the form of truncated exponentials.
+
+The membrane potential is given by the following differential equation:
+
+.. math::
+
+   C_m \frac{dV_m}{dt} =
+   -g_L(V_m-E_L)+g_L\Delta_T\exp\left(\frac{V_m-V_{th}}{\Delta_T}\right) - g_e(t)(V_m-E_e) \\
+                                                     -g_i(t)(V_m-E_i)-w +I_e
+
+and
+
+.. math::
+
+   \tau_w \frac{dw}{dt} = a(V_m-E_L) - w
+
+Note that the membrane potential can diverge to positive infinity due to the exponential term. To avoid numerical instabilities, instead of :math:`V_m`, the value :math:`\min(V_m,V_{peak})` is used in the dynamical equations.
+
+
+References
+++++++++++
+
+.. [1] Brette R and Gerstner W (2005). Adaptive exponential
+       integrate-and-fire model as an effective description of neuronal
+       activity. Journal of Neurophysiology. 943637-3642
+       DOI: https://doi.org/10.1152/jn.00686.2005
+
+
+See also
+++++++++
+
+iaf_cond_exp, aeif_cond_alpha
+
+
+
+Parameters
+++++++++++
+.. csv-table::
+    :header: "Name", "Physical unit", "Default value", "Description"
+    :widths: auto
+
+
+    "C_m", "pF", "281.0pF", "membrane parametersMembrane Capacitance"    
+    "refr_T", "ms", "2ms", "Duration of refractory period"    
+    "V_reset", "mV", "-60.0mV", "Reset Potential"    
+    "g_L", "nS", "30.0nS", "Leak Conductance"    
+    "E_L", "mV", "-70.6mV", "Leak reversal Potential (aka resting potential)"    
+    "a", "nS", "4nS", "spike adaptation parametersSubthreshold adaptation"    
+    "b", "pA", "80.5pA", "Spike-triggered adaptation"    
+    "Delta_T", "mV", "2.0mV", "Slope factor"    
+    "tau_w", "ms", "144.0ms", "Adaptation time constant"    
+    "V_th", "mV", "-50.4mV", "Threshold Potential"    
+    "V_peak", "mV", "0mV", "Spike detection threshold"    
+    "E_exc", "mV", "0mV", "synaptic parametersExcitatory reversal Potential"    
+    "tau_syn_exc", "ms", "0.2ms", "Synaptic Time Constant Excitatory Synapse"    
+    "E_inh", "mV", "-85.0mV", "Inhibitory reversal Potential"    
+    "tau_syn_inh", "ms", "2.0ms", "Synaptic Time Constant for Inhibitory Synapse"    
+    "I_e", "pA", "0pA", "constant external input current"
+
+
+
+State variables
++++++++++++++++
+
+.. csv-table::
+    :header: "Name", "Physical unit", "Default value", "Description"
+    :widths: auto
+
+
+    "V_m", "mV", "E_L", "Membrane potential"    
+    "w", "pA", "0pA", "Spike-adaptation current"    
+    "refr_t", "ms", "0ms", "Refractory period timer"    
+    "is_refractory", "boolean", "false", ""
+
+
+
+
+Equations
++++++++++
+
+
+
+.. math::
+   \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-g_{L} \cdot (V_{bounded} - E_{L}) + I_{spike} - I_{syn,exc} - I_{syn,inh} - w + I_{e} + I_{stim}) } \right) 
+
+.. math::
+   \frac{ dw } { dt }= \frac 1 { \tau_{w} } \left( { (a \cdot (V_{bounded} - E_{L}) - w) } \right) 
+
+
+
+Source code
++++++++++++
+
+The model source code can be found in the NESTML models repository here: `aeif_cond_exp_neuron <https://github.com/nest/nestml/tree/master/models/neurons/aeif_cond_exp_neuron.nestml>`_.
+
+Characterisation
+++++++++++++++++
+
+.. include:: aeif_cond_exp_neuron_characterisation.rst
+
+
+.. footer::
+
+   Generated at 2024-05-14 22:01:11.765292

doc/models_library/hh_cond_exp_destexhe_neuron.rst

@@ -0,0 +1,131 @@
+hh_cond_exp_destexhe_neuron
+###########################
+
+
+hh_cond_exp_destexhe - Hodgin Huxley based model, Traub, Destexhe and Mainen modified
+
+Description
++++++++++++
+
+hh_cond_exp_destexhe is an implementation of a modified Hodkin-Huxley model, which is based on the hh_cond_exp_traub model.
+
+Differences to hh_cond_exp_traub:
+
+(1) **Additional background noise:** A background current whose conductances were modeled as an Ornstein-Uhlenbeck process is injected into the neuron.
+(2) **Additional non-inactivating K+ current:** A non-inactivating K+ current was included, which is responsible for spike frequency adaptation.
+
+
+References
+++++++++++
+
+.. [1] Traub, R.D. and Miles, R. (1991) Neuronal Networks of the Hippocampus. Cambridge University Press, Cambridge UK.
+
+.. [2] Destexhe, A. and Pare, D. (1999) Impact of Network Activity on the Integrative Properties of Neocortical Pyramidal Neurons In Vivo. Journal of Neurophysiology
+
+.. [3] A. Destexhe, M. Rudolph, J.-M. Fellous and T. J. Sejnowski (2001) Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience
+
+.. [4] Z. Mainen, J. Joerges, J. R. Huguenard and T. J. Sejnowski (1995) A Model of Spike Initiation in Neocortical Pyramidal Neurons. Neuron
+
+
+See also
+++++++++
+
+hh_cond_exp_traub
+
+
+
+Parameters
+++++++++++
+.. csv-table::
+    :header: "Name", "Physical unit", "Default value", "Description"
+    :widths: auto
+
+
+    "g_Na", "nS", "17318.0nS", "Na Conductance"    
+    "g_K", "nS", "3463.6nS", "K Conductance"    
+    "g_L", "nS", "15.5862nS", "Leak Conductance"    
+    "C_m", "pF", "346.36pF", "Membrane Capacitance"    
+    "E_Na", "mV", "60mV", "Reversal potentials"    
+    "E_K", "mV", "-90.0mV", "Potassium reversal potential"    
+    "E_L", "mV", "-80.0mV", "Leak reversal Potential (aka resting potential)"    
+    "V_T", "mV", "-58.0mV", "Voltage offset that controls dynamics. For default"    
+    "tau_syn_exc", "ms", "2.7ms", "parameters, V_T = -63mV results in a threshold around -50mV.Synaptic Time Constant Excitatory Synapse"    
+    "tau_syn_inh", "ms", "10.5ms", "Synaptic Time Constant for Inhibitory Synapse"    
+    "E_exc", "mV", "0.0mV", "Excitatory synaptic reversal potential"    
+    "E_inh", "mV", "-75.0mV", "Inhibitory synaptic reversal potential"    
+    "g_M", "nS", "173.18nS", "Conductance of non-inactivating K+ channel"    
+    "g_noise_exc0", "uS", "0.012uS", "Conductance OU noiseMean of the excitatory noise conductance"    
+    "g_noise_inh0", "uS", "0.057uS", "Mean of the inhibitory noise conductance"    
+    "sigma_noise_exc", "uS", "0.003uS", "Standard deviation of the excitatory noise conductance"    
+    "sigma_noise_inh", "uS", "0.0066uS", "Standard deviation of the inhibitory noise conductance"    
+    "alpha_n_init", "1 / ms", "0.032 / (ms * mV) * (15.0mV - V_m) / (exp((15.0mV - V_m) / 5.0mV) - 1.0)", ""    
+    "beta_n_init", "1 / ms", "0.5 / ms * exp((10.0mV - V_m) / 40.0mV)", ""    
+    "alpha_m_init", "1 / ms", "0.32 / (ms * mV) * (13.0mV - V_m) / (exp((13.0mV - V_m) / 4.0mV) - 1.0)", ""    
+    "beta_m_init", "1 / ms", "0.28 / (ms * mV) * (V_m - 40.0mV) / (exp((V_m - 40.0mV) / 5.0mV) - 1.0)", ""    
+    "alpha_h_init", "1 / ms", "0.128 / ms * exp((17.0mV - V_m) / 18.0mV)", ""    
+    "beta_h_init", "1 / ms", "(4.0 / (1.0 + exp((40.0mV - V_m) / 5.0mV))) / ms", ""    
+    "alpha_p_init", "1 / ms", "0.0001 / (ms * mV) * (V_m + 30.0mV) / (1.0 - exp(-(V_m + 30.0mV) / 9.0mV))", ""    
+    "beta_p_init", "1 / ms", "-0.0001 / (ms * mV) * (V_m + 30.0mV) / (1.0 - exp((V_m + 30.0mV) / 9.0mV))", ""    
+    "refr_T", "ms", "2ms", "Duration of refractory period"    
+    "I_e", "pA", "0pA", "constant external input current"
+
+
+
+State variables
++++++++++++++++
+
+.. csv-table::
+    :header: "Name", "Physical unit", "Default value", "Description"
+    :widths: auto
+
+
+    "g_noise_exc", "uS", "g_noise_exc0", ""    
+    "g_noise_inh", "uS", "g_noise_inh0", ""    
+    "V_m", "mV", "E_L", "Membrane potential"    
+    "V_m_old", "mV", "E_L", "Membrane potential at the previous timestep"    
+    "refr_t", "ms", "0ms", "Refractory period timer"    
+    "is_refractory", "boolean", "false", ""    
+    "Act_m", "real", "alpha_m_init / (alpha_m_init + beta_m_init)", ""    
+    "Act_h", "real", "alpha_h_init / (alpha_h_init + beta_h_init)", ""    
+    "Inact_n", "real", "alpha_n_init / (alpha_n_init + beta_n_init)", ""    
+    "Noninact_p", "real", "alpha_p_init / (alpha_p_init + beta_p_init)", ""
+
+
+
+
+Equations
++++++++++
+
+
+
+.. math::
+   \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-I_{Na} - I_{K} - I_{M} - I_{L} - I_{syn,exc} - I_{syn,inh} + I_{e} + I_{stim} - I_{noise}) } \right) 
+
+.. math::
+   \frac{ dAct_{m} } { dt }= (\alpha_{m} - (\alpha_{m} + \beta_{m}) \cdot Act_{m})
+
+.. math::
+   \frac{ dAct_{h} } { dt }= (\alpha_{h} - (\alpha_{h} + \beta_{h}) \cdot Act_{h})
+
+.. math::
+   \frac{ dInact_{n} } { dt }= (\alpha_{n} - (\alpha_{n} + \beta_{n}) \cdot Inact_{n})
+
+.. math::
+   \frac{ dNoninact_{p} } { dt }= (\alpha_{p} - (\alpha_{p} + \beta_{p}) \cdot Noninact_{p})
+
+
+
+Source code
++++++++++++
+
+The model source code can be found in the NESTML models repository here: `hh_cond_exp_destexhe_neuron <https://github.com/nest/nestml/tree/master/models/neurons/hh_cond_exp_destexhe_neuron.nestml>`_.
+
+Characterisation
+++++++++++++++++
+
+.. include:: hh_cond_exp_destexhe_neuron_characterisation.rst
+
+
+.. footer::
+
+   Generated at 2024-05-14 22:01:11.772062