From d38408518cdea313ed83e96ae6d3a220676b4430 Mon Sep 17 00:00:00 2001 From: "C.A.P. Linssen" Date: Wed, 15 May 2024 00:02:15 +0200 Subject: [PATCH] automatic model characterisation and doc generator --- doc/models_library/.aeif_cond_alpha.rst.swp | Bin 0 -> 1024 bytes doc/models_library/aeif_cond_alpha_neuron.rst | 114 +++++++++++++ doc/models_library/aeif_cond_exp_neuron.rst | 115 +++++++++++++ .../hh_cond_exp_destexhe_neuron.rst | 131 +++++++++++++++ .../hh_cond_exp_traub_neuron.rst | 135 +++++++++++++++ doc/models_library/hh_moto_5ht_neuron.rst | 139 ++++++++++++++++ doc/models_library/hh_psc_alpha_neuron.rst | 123 ++++++++++++++ doc/models_library/hill_tononi_neuron.rst | 155 ++++++++++++++++++ doc/models_library/iaf_chxk_2008_neuron.rst | 105 ++++++++++++ doc/models_library/iaf_cond_alpha_neuron.rst | 101 ++++++++++++ doc/models_library/iaf_cond_beta_neuron.rst | 117 +++++++++++++ doc/models_library/iaf_cond_exp_neuron.rst | 91 ++++++++++ .../iaf_cond_exp_sfa_rr_neuron.rst | 113 +++++++++++++ doc/models_library/iaf_psc_alpha_neuron.rst | 120 ++++++++++++++ doc/models_library/iaf_psc_delta_neuron.rst | 104 ++++++++++++ .../iaf_psc_exp_dend_neuron.rst | 100 +++++++++++ .../iaf_psc_exp_htum_neuron.rst | 115 +++++++++++++ doc/models_library/iaf_psc_exp_neuron.rst | 124 ++++++++++++++ doc/models_library/ignore_and_fire_neuron.rst | 73 +++++++++ doc/models_library/izhikevich_neuron.rst | 101 ++++++++++++ .../izhikevich_psc_alpha_neuron.rst | 108 ++++++++++++ doc/models_library/mat2_psc_exp_neuron.rst | 111 +++++++++++++ .../neuromodulated_stdp_synapse.rst | 2 +- doc/models_library/noisy_synapse.rst | 2 +- doc/models_library/static_synapse.rst | 2 +- .../stdp_nn_pre_centered_synapse.rst | 2 +- .../stdp_nn_restr_symm_synapse.rst | 2 +- doc/models_library/stdp_nn_symm_synapse.rst | 2 +- doc/models_library/stdp_synapse.rst | 2 +- doc/models_library/stdp_triplet_synapse.rst | 72 ++++++++ doc/models_library/terub_gpe_neuron.rst | 117 +++++++++++++ doc/models_library/terub_stn_neuron.rst | 113 +++++++++++++ .../third_factor_stdp_synapse.rst | 2 +- .../traub_cond_multisyn_neuron.rst | 129 +++++++++++++++ doc/models_library/traub_psc_alpha_neuron.rst | 101 ++++++++++++ doc/models_library/wb_cond_exp_neuron.rst | 104 ++++++++++++ .../wb_cond_multisyn_neuron.rst | 126 ++++++++++++++ .../codegeneration/autodoc_code_generator.py | 5 +- 38 files changed, 3167 insertions(+), 11 deletions(-) create mode 100644 doc/models_library/.aeif_cond_alpha.rst.swp create mode 100644 doc/models_library/aeif_cond_alpha_neuron.rst create mode 100644 doc/models_library/aeif_cond_exp_neuron.rst create mode 100644 doc/models_library/hh_cond_exp_destexhe_neuron.rst create mode 100644 doc/models_library/hh_cond_exp_traub_neuron.rst create mode 100644 doc/models_library/hh_moto_5ht_neuron.rst create mode 100644 doc/models_library/hh_psc_alpha_neuron.rst create mode 100644 doc/models_library/hill_tononi_neuron.rst create mode 100644 doc/models_library/iaf_chxk_2008_neuron.rst create mode 100644 doc/models_library/iaf_cond_alpha_neuron.rst create mode 100644 doc/models_library/iaf_cond_beta_neuron.rst create mode 100644 doc/models_library/iaf_cond_exp_neuron.rst create mode 100644 doc/models_library/iaf_cond_exp_sfa_rr_neuron.rst create mode 100644 doc/models_library/iaf_psc_alpha_neuron.rst create mode 100644 doc/models_library/iaf_psc_delta_neuron.rst create mode 100644 doc/models_library/iaf_psc_exp_dend_neuron.rst create mode 100644 doc/models_library/iaf_psc_exp_htum_neuron.rst create mode 100644 doc/models_library/iaf_psc_exp_neuron.rst create mode 100644 doc/models_library/ignore_and_fire_neuron.rst create mode 100644 doc/models_library/izhikevich_neuron.rst create mode 100644 doc/models_library/izhikevich_psc_alpha_neuron.rst create mode 100644 doc/models_library/mat2_psc_exp_neuron.rst create mode 100644 doc/models_library/stdp_triplet_synapse.rst create mode 100644 doc/models_library/terub_gpe_neuron.rst create mode 100644 doc/models_library/terub_stn_neuron.rst create mode 100644 doc/models_library/traub_cond_multisyn_neuron.rst create mode 100644 doc/models_library/traub_psc_alpha_neuron.rst create mode 100644 doc/models_library/wb_cond_exp_neuron.rst create mode 100644 doc/models_library/wb_cond_multisyn_neuron.rst diff --git a/doc/models_library/.aeif_cond_alpha.rst.swp b/doc/models_library/.aeif_cond_alpha.rst.swp new file mode 100644 index 0000000000000000000000000000000000000000..8630095247d2e7f4e4d70e21bb54411ffe699cab GIT binary patch literal 1024 zcmYc?$V<%2S1{KzVn6{ATNxRWGZKq(P(-nFGV_Y_bMrD2ld(&~MN{&V^>g!6Qge#q rb25{P5{oMJ6H_zO;*<09QsNVH3NjM)ii%6%%10TaAut*OG!6j(E#Vly literal 0 HcmV?d00001 diff --git a/doc/models_library/aeif_cond_alpha_neuron.rst b/doc/models_library/aeif_cond_alpha_neuron.rst new file mode 100644 index 000000000..575c9621f --- /dev/null +++ b/doc/models_library/aeif_cond_alpha_neuron.rst @@ -0,0 +1,114 @@ +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 `_. + +Characterisation +++++++++++++++++ + +.. include:: aeif_cond_alpha_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.810891 \ No newline at end of file diff --git a/doc/models_library/aeif_cond_exp_neuron.rst b/doc/models_library/aeif_cond_exp_neuron.rst new file mode 100644 index 000000000..cb696e93d --- /dev/null +++ b/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 `_. + +Characterisation +++++++++++++++++ + +.. include:: aeif_cond_exp_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.765292 \ No newline at end of file diff --git a/doc/models_library/hh_cond_exp_destexhe_neuron.rst b/doc/models_library/hh_cond_exp_destexhe_neuron.rst new file mode 100644 index 000000000..46ef54226 --- /dev/null +++ b/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 `_. + +Characterisation +++++++++++++++++ + +.. include:: hh_cond_exp_destexhe_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.772062 \ No newline at end of file diff --git a/doc/models_library/hh_cond_exp_traub_neuron.rst b/doc/models_library/hh_cond_exp_traub_neuron.rst new file mode 100644 index 000000000..8e6f034b3 --- /dev/null +++ b/doc/models_library/hh_cond_exp_traub_neuron.rst @@ -0,0 +1,135 @@ +hh_cond_exp_traub_neuron +######################## + + +hh_cond_exp_traub - Hodgkin-Huxley model for Brette et al (2007) review + +Description ++++++++++++ + +hh_cond_exp_traub is an implementation of a modified Hodgkin-Huxley model. + +This model was specifically developed for a major review of simulators [1]_, +based on a model of hippocampal pyramidal cells by Traub and Miles [2]_. +The key differences between the current model and the model in [2]_ are: + +- This model is a point neuron, not a compartmental model. +- This model includes only I_Na and I_K, with simpler I_K dynamics than + in [2]_, so it has only three instead of eight gating variables; + in particular, all Ca dynamics have been removed. +- Incoming spikes induce an instantaneous conductance change followed by + exponential decay instead of activation over time. + +This model is primarily provided as reference implementation for hh_coba +example of the Brette et al (2007) review. Default parameter values are chosen +to match those used with NEST 1.9.10 when preparing data for [1]_. Code for all +simulators covered is available from ModelDB [3]_. + +Note: In this model, a spike is emitted if :math:`V_m >= V_T + 30` mV and +:math:`V_m` has fallen during the current time step. + +To avoid that this leads to multiple spikes during the falling flank of a +spike, it is essential to choose a sufficiently long refractory period. +Traub and Miles used :math:`t_{ref} = 3` ms [2, p 118], while we used +:math:`t_{ref} = 2` ms in [2]_. + +References +++++++++++ + +.. [1] Brette R et al. (2007). Simulation of networks of spiking neurons: A + review of tools and strategies. Journal of Computational Neuroscience + 23:349-98. DOI: https://doi.org/10.1007/s10827-007-0038-6 +.. [2] Traub RD and Miles R (1991). Neuronal networks of the hippocampus. + Cambridge University Press, Cambridge UK. +.. [3] http://modeldb.yale.edu/83319 + + +See also +++++++++ + +hh_psc_alpha + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "g_Na", "nS", "20000nS", "Na Conductance" + "g_K", "nS", "6000nS", "K Conductance" + "g_L", "nS", "10nS", "Leak Conductance" + "C_m", "pF", "200pF", "Membrane Capacitance" + "E_Na", "mV", "50mV", "Reversal potentials" + "E_K", "mV", "-90mV", "Potassium reversal potential" + "E_L", "mV", "-60mV", "Leak reversal potential (aka resting potential)" + "V_T", "mV", "-63mV", "Voltage offset that controls dynamics. For default" + "tau_syn_exc", "ms", "5ms", "parameters, V_T = -63 mV results in a threshold around -50 mV.Synaptic time constant of excitatory synapse" + "tau_syn_inh", "ms", "10ms", "Synaptic time constant of inhibitory synapse" + "refr_T", "ms", "2ms", "Duration of refractory period" + "E_exc", "mV", "0mV", "Excitatory synaptic reversal potential" + "E_inh", "mV", "-80mV", "Inhibitory synaptic reversal potential" + "alpha_n_init", "1 / ms", "0.032 / (ms * mV) * (15.0mV - E_L) / (exp((15.0mV - E_L) / 5.0mV) - 1.0)", "" + "beta_n_init", "1 / ms", "0.5 / ms * exp((10.0mV - E_L) / 40.0mV)", "" + "alpha_m_init", "1 / ms", "0.32 / (ms * mV) * (13.0mV - E_L) / (exp((13.0mV - E_L) / 4.0mV) - 1.0)", "" + "beta_m_init", "1 / ms", "0.28 / (ms * mV) * (E_L - 40.0mV) / (exp((E_L - 40.0mV) / 5.0mV) - 1.0)", "" + "alpha_h_init", "1 / ms", "0.128 / ms * exp((17.0mV - E_L) / 18.0mV)", "" + "beta_h_init", "1 / ms", "(4.0 / (1.0 + exp((40.0mV - E_L) / 5.0mV))) / ms", "" + "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" + "V_m_old", "mV", "E_L", "Membrane potential at 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)", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-I_{Na} - I_{K} - I_{L} - I_{syn,exc} - I_{syn,inh} + I_{e} + I_{stim}) } \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}) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `hh_cond_exp_traub_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: hh_cond_exp_traub_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.925062 \ No newline at end of file diff --git a/doc/models_library/hh_moto_5ht_neuron.rst b/doc/models_library/hh_moto_5ht_neuron.rst new file mode 100644 index 000000000..3a4e165df --- /dev/null +++ b/doc/models_library/hh_moto_5ht_neuron.rst @@ -0,0 +1,139 @@ +hh_moto_5ht_neuron +################## + + +hh_moto_5ht_nestml - a motor neuron model in HH formalism with 5HT modulation + +Description ++++++++++++ + +hh_moto_5ht is an implementation of a spiking motor neuron using the Hodgkin-Huxley formalism according to [2]_. Basically this model is an implementation of the existing NEURON model [1]_. + +The parameter that represents 5HT modulation is ``g_K_Ca_5ht``. When it equals 1, no modulation happens. An application of 5HT corresponds to its decrease. The default value for it is 0.6. This value was used in the Neuron simulator model. The range of this parameter is (0, 1] but you are free to play with any value. + +Post-synaptic currents and spike detection are the same as in hh_psc_alpha. + + +References +++++++++++ + +.. [1] Muscle spindle feedback circuit by Moraud EM and Capogrosso M. + https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=189786 + +.. [2] Compartmental model of vertebrate motoneurons for Ca2+-dependent spiking and plateau potentials under pharmacological treatment. + Booth V, Rinzel J, Kiehn O. + http://refhub.elsevier.com/S0896-6273(16)00010-6/sref4 + +.. [3] Repository: https://github.com/research-team/hh-moto-5ht + + +See also +++++++++ + +hh_psc_alpha + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "refr_T", "ms", "2ms", "Duration of refractory period" + "g_Na", "nS", "5000.0nS", "Sodium peak conductance" + "g_L", "nS", "200.0nS", "Leak conductance" + "g_K_rect", "nS", "30000.0nS", "Delayed Rectifier Potassium peak conductance" + "g_Ca_N", "nS", "5000.0nS", "" + "g_Ca_L", "nS", "10.0nS", "" + "g_K_Ca", "nS", "30000.0nS", "" + "g_K_Ca_5ht", "real", "0.6", "modulation of K-Ca channels by 5HT. Its value 1.0 == no modulation." + "Ca_in_init", "mmol", "0.0001mmol", "Initial inside Calcium concentration" + "Ca_out", "mmol", "2.0mmol", "Outside Calcium concentration. Remains constant during simulation." + "C_m", "pF", "200.0pF", "Membrane capacitance" + "E_Na", "mV", "50.0mV", "" + "E_K", "mV", "-80.0mV", "" + "E_L", "mV", "-70.0mV", "" + "R_const", "real", "8.314472", "Nernst equation constants" + "F_const", "real", "96485.34", "" + "T_current", "real", "309.15", "36 Celcius" + "tau_syn_ex", "ms", "0.2ms", "Rise time of the excitatory synaptic alpha function" + "tau_syn_in", "ms", "2.0ms", "Rise time of the inhibitory synaptic alpha function" + "I_e", "pA", "0pA", "Constant current" + "V_m_init", "mV", "-65.0mV", "" + "hc_tau", "ms", "50.0ms", "" + "mc_tau", "ms", "15.0ms", "" + "p_tau", "ms", "400.0ms", "" + "alpha", "mmol / pA", "1e-05mmol / pA", "" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m", "mV", "V_m_init", "Membrane potential" + "V_m_old", "mV", "V_m_init", "Membrane potential" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "Ca_in", "mmol", "Ca_in_init", "Inside Calcium concentration" + "Act_m", "real", "alpha_m(V_m_init) / (alpha_m(V_m_init) + beta_m(V_m_init))", "" + "Act_h", "real", "h_inf(V_m_init)", "" + "Inact_n", "real", "n_inf(V_m_init)", "" + "Act_p", "real", "p_inf(V_m_init)", "" + "Act_mc", "real", "mc_inf(V_m_init)", "" + "Act_hc", "real", "hc_inf(V_m_init)", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-(I_{Na} + I_{K} + I_{L} + I_{Ca,N} + I_{Ca,L} + I_{K,Ca}) + I_{stim} + I_{e} + I_{syn,inh} + I_{syn,exc}) } \right) + +.. math:: + \frac{ dInact_{n} } { dt }= \frac{ (\text{n_inf}(V_{m}) - Inact_{n}) } { \text{n_tau}(V_{m}) } + +.. math:: + \frac{ dAct_{m} } { dt }= \text{alpha_m}(V_{m}) \cdot (1.0 - Act_{m}) - \text{beta_m}(V_{m}) \cdot Act_{m} + +.. math:: + \frac{ dAct_{h} } { dt }= \frac{ (\text{h_inf}(V_{m}) - Act_{h}) } { \text{h_tau}(V_{m}) } + +.. math:: + \frac{ dAct_{p} } { dt }= \frac 1 { p_{\tau} } \left( { (\text{p_inf}(V_{m}) - Act_{p}) } \right) + +.. math:: + \frac{ dAct_{mc} } { dt }= \frac 1 { mc_{\tau} } \left( { (\text{mc_inf}(V_{m}) - Act_{mc}) } \right) + +.. math:: + \frac{ dAct_{hc} } { dt }= \frac 1 { hc_{\tau} } \left( { (\text{hc_inf}(V_{m}) - Act_{hc}) } \right) + +.. math:: + \frac{ dCa_{in} } { dt }= (\frac{ 0.01 } { \mathrm{s} }) \cdot (-\alpha \cdot (I_{Ca,N} + I_{Ca,L}) - 4.0 \cdot Ca_{in}) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `hh_moto_5ht_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: hh_moto_5ht_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.899817 \ No newline at end of file diff --git a/doc/models_library/hh_psc_alpha_neuron.rst b/doc/models_library/hh_psc_alpha_neuron.rst new file mode 100644 index 000000000..f2c63a6d7 --- /dev/null +++ b/doc/models_library/hh_psc_alpha_neuron.rst @@ -0,0 +1,123 @@ +hh_psc_alpha_neuron +################### + + +hh_psc_alpha - Hodgkin-Huxley neuron model + +Description ++++++++++++ + +hh_psc_alpha is an implementation of a spiking neuron using the Hodgkin-Huxley +formalism. + +Incoming spike events induce a post-synaptic change of current modelled +by an alpha function. The alpha function is normalised such that an event of +weight 1.0 results in a peak current of 1 pA. + +Spike detection is done by a combined threshold-and-local-maximum search: if +there is a local maximum above a certain threshold of the membrane potential, +it is considered a spike. + + +Problems/Todo ++++++++++++++ + +- better spike detection +- initial wavelet/spike at simulation onset + + +References +++++++++++ + +.. [1] Gerstner W, Kistler W (2002). Spiking neuron models: Single neurons, + populations, plasticity. New York: Cambridge University Press +.. [2] Dayan P, Abbott LF (2001). Theoretical neuroscience: Computational and + mathematical modeling of neural systems. Cambridge, MA: MIT Press. + https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3006127> +.. [3] Hodgkin AL and Huxley A F (1952). A quantitative description of + membrane current and its application to conduction and excitation in + nerve. The Journal of Physiology 117. + DOI: https://doi.org/10.1113/jphysiol.1952.sp004764 + + +See also +++++++++ + +hh_cond_exp_traub + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m_init", "mV", "-65mV", "Initial membrane potential" + "C_m", "pF", "100pF", "Membrane Capacitance" + "g_Na", "nS", "12000nS", "Sodium peak conductance" + "g_K", "nS", "3600nS", "Potassium peak conductance" + "g_L", "nS", "30nS", "Leak conductance" + "E_Na", "mV", "50mV", "Sodium reversal potential" + "E_K", "mV", "-77mV", "Potassium reversal potential" + "E_L", "mV", "-54.402mV", "Leak reversal Potential (aka resting potential)" + "refr_T", "ms", "2ms", "Duration of refractory period" + "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 ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m", "mV", "V_m_init", "Membrane potential" + "V_m_old", "mV", "V_m_init", "Membrane potential at previous timestep for threshold check" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "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 ++++++++++ + + + +.. math:: + \frac{ dAct_{n} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{n} \cdot (1 - Act_{n}) - \beta_{n} \cdot Act_{n}) } \right) + +.. math:: + \frac{ dAct_{m} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{m} \cdot (1 - Act_{m}) - \beta_{m} \cdot Act_{m}) } \right) + +.. math:: + \frac{ dInact_{h} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{h} \cdot (1 - Inact_{h}) - \beta_{h} \cdot Inact_{h}) } \right) + +.. math:: + \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) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `hh_psc_alpha_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: hh_psc_alpha_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.732901 \ No newline at end of file diff --git a/doc/models_library/hill_tononi_neuron.rst b/doc/models_library/hill_tononi_neuron.rst new file mode 100644 index 000000000..5af9b732f --- /dev/null +++ b/doc/models_library/hill_tononi_neuron.rst @@ -0,0 +1,155 @@ +hill_tononi_neuron +################## + + +hill_tononi - Neuron model after Hill & Tononi (2005) + +Description ++++++++++++ + +This model neuron implements a slightly modified version of the +neuron model described in [1]_. The most important properties are: + +- Integrate-and-fire with threshold adaptive threshold. +- Repolarizing potassium current instead of hard reset. +- AMPA, NMDA, GABA_A, and GABA_B conductance-based synapses with + beta-function (difference of exponentials) time course. +- Voltage-dependent NMDA with instantaneous or two-stage unblocking [1]_, [2]_. +- Intrinsic currents I_h, I_T, I_Na(p), and I_KNa. +- Synaptic "minis" are not implemented. + +Documentation and examples can be found on the NEST Simulator repository +(https://github.com/nest/nest-simulator/) at the following paths: +- docs/model_details/HillTononiModels.ipynb +- pynest/examples/intrinsic_currents_spiking.py +- pynest/examples/intrinsic_currents_subthreshold.py + + +References +++++++++++ + +.. [1] Hill S, Tononi G (2005). Modeling sleep and wakefulness in the + thalamocortical system. Journal of Neurophysiology. 93:1671-1698. + DOI: https://doi.org/10.1152/jn.00915.2004 +.. [2] Vargas-Caballero M, Robinson HPC (2003). A slow fraction of Mg2+ + unblock of NMDA receptors limits their contribution to spike generation + in cortical pyramidal neurons. Journal of Neurophysiology 89:2778-2783. + DOI: https://doi.org/10.1152/jn.01038.2002 + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "E_Na", "mV", "30.0mV", "" + "E_K", "mV", "-90.0mV", "" + "g_NaL", "nS", "0.2nS", "" + "g_KL", "nS", "1.0nS", "1.0 - 1.85" + "Tau_m", "ms", "16.0ms", "membrane time constant applying to all currents but repolarizing K-current (see [1, p 1677])" + "Theta_eq", "mV", "-51.0mV", "equilibrium value" + "Tau_theta", "ms", "2.0ms", "time constant" + "Tau_spike", "ms", "1.75ms", "membrane time constant applying to repolarizing K-current" + "t_spike", "ms", "2.0ms", "duration of re-polarizing potassium current" + "AMPA_g_peak", "nS", "0.1nS", "Parameters for synapse of type AMPA, GABA_A, GABA_B and NMDApeak conductance" + "AMPA_E_rev", "mV", "0.0mV", "reversal potential" + "AMPA_Tau_1", "ms", "0.5ms", "rise time" + "AMPA_Tau_2", "ms", "2.4ms", "decay time, Tau_1 < Tau_2" + "NMDA_g_peak", "nS", "0.075nS", "peak conductance" + "NMDA_Tau_1", "ms", "4.0ms", "rise time" + "NMDA_Tau_2", "ms", "40.0ms", "decay time, Tau_1 < Tau_2" + "NMDA_E_rev", "mV", "0.0mV", "reversal potential" + "NMDA_Vact", "mV", "-58.0mV", "inactive for V << Vact, inflection of sigmoid" + "NMDA_Sact", "mV", "2.5mV", "scale of inactivation" + "GABA_A_g_peak", "nS", "0.33nS", "peak conductance" + "GABA_A_Tau_1", "ms", "1.0ms", "rise time" + "GABA_A_Tau_2", "ms", "7.0ms", "decay time, Tau_1 < Tau_2" + "GABA_A_E_rev", "mV", "-70.0mV", "reversal potential" + "GABA_B_g_peak", "nS", "0.0132nS", "peak conductance" + "GABA_B_Tau_1", "ms", "60.0ms", "rise time" + "GABA_B_Tau_2", "ms", "200.0ms", "decay time, Tau_1 < Tau_2" + "GABA_B_E_rev", "mV", "-90.0mV", "reversal potential for intrinsic current" + "NaP_g_peak", "nS", "1.0nS", "parameters for intrinsic currentspeak conductance for intrinsic current" + "NaP_E_rev", "mV", "30.0mV", "reversal potential for intrinsic current" + "KNa_g_peak", "nS", "1.0nS", "peak conductance for intrinsic current" + "KNa_E_rev", "mV", "-90.0mV", "reversal potential for intrinsic current" + "T_g_peak", "nS", "1.0nS", "peak conductance for intrinsic current" + "T_E_rev", "mV", "0.0mV", "reversal potential for intrinsic current" + "h_g_peak", "nS", "1.0nS", "peak conductance for intrinsic current" + "h_E_rev", "mV", "-40.0mV", "reversal potential for intrinsic current" + "KNa_D_EQ", "pA", "0.001pA", "" + "I_e", "pA", "0pA", "constant external input current" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "potassium_refr_t", "ms", "0ms", "" + "is_refractory", "boolean", "false", "" + "g_spike", "boolean", "false", "" + "V_m", "mV", "(g_NaL * E_Na + g_KL * E_K) / (g_NaL + g_KL)", "membrane potential" + "Theta", "mV", "Theta_eq", "Threshold" + "IKNa_D", "nS", "0.0nS", "" + "IT_m", "nS", "0.0nS", "" + "IT_h", "nS", "0.0nS", "" + "Ih_m", "nS", "0.0nS", "" + "g_AMPA", "real", "0", "" + "g_NMDA", "real", "0", "" + "g_GABAA", "real", "0", "" + "g_GABAB", "real", "0", "" + "g_AMPA$", "real", "AMPAInitialValue", "" + "g_NMDA$", "real", "NMDAInitialValue", "" + "g_GABAA$", "real", "GABA_AInitialValue", "" + "g_GABAB$", "real", "GABA_BInitialValue", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { \mathrm{nF} } \left( { (\frac 1 { \Tau_{m} } \left( { (I_{Na} + I_{K} + I_{syn} + I_{NaP} + I_{KNa} + I_{T} + I_{h} + I_{e} + I_{stim}) } \right) + \frac{ I_{spike} \cdot \mathrm{pA} } { (\mathrm{ms} \cdot \mathrm{mV}) }) \cdot \mathrm{s} } \right) + +.. math:: + \frac{ d\Theta } { dt }= \frac{ -(\Theta - \Theta_{eq}) } { \Tau_{\theta} } + +.. math:: + \frac{ dIKNa_{D} } { dt }= \frac 1 { \mathrm{ms} } \left( { (D_{influx,peak} \cdot D_{influx} \cdot \mathrm{nS} - \frac 1 { \tau_{D} } \left( { (IKNa_{D} - \frac{ KNa_{D,EQ} } { \mathrm{mV} }) } \right) ) } \right) + +.. math:: + \frac{ dIT_{m} } { dt }= \frac 1 { \mathrm{ms} } \left( { \frac 1 { \tau_{m,T} } \left( { (m_{\infty,T} \cdot \mathrm{nS} - IT_{m}) } \right) } \right) + +.. math:: + \frac{ dIT_{h} } { dt }= \frac 1 { \mathrm{ms} } \left( { \frac 1 { \tau_{h,T} } \left( { (h_{\infty,T} \cdot \mathrm{nS} - IT_{h}) } \right) } \right) + +.. math:: + \frac{ dIh_{m} } { dt }= \frac 1 { \mathrm{ms} } \left( { \frac 1 { \tau_{m,h} } \left( { (m_{\infty,h} \cdot \mathrm{nS} - Ih_{m}) } \right) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `hill_tononi_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: hill_tononi_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.817015 \ No newline at end of file diff --git a/doc/models_library/iaf_chxk_2008_neuron.rst b/doc/models_library/iaf_chxk_2008_neuron.rst new file mode 100644 index 000000000..f66db1ba7 --- /dev/null +++ b/doc/models_library/iaf_chxk_2008_neuron.rst @@ -0,0 +1,105 @@ +iaf_chxk_2008_neuron +#################### + + +iaf_chxk_2008 - Conductance based leaky integrate-and-fire neuron model used in Casti et al. 2008 + +Description ++++++++++++ + +iaf_chxk_2008 is an implementation of a spiking neuron using IAF dynamics with +conductance-based synapses [1]_. A spike is emitted when the membrane potential +is crossed from below. After a spike, an afterhyperpolarizing (AHP) conductance +is activated which repolarizes the neuron over time. Membrane potential is not +reset explicitly and the model also has no explicit refractory time. + +The AHP conductance and excitatory and inhibitory synaptic input conductances +follow alpha-function time courses as in the iaf_cond_alpha model. + +.. note :: + In the original Fortran implementation underlying [1]_, all previous AHP activation was discarded when a new spike + occurred, leading to reduced AHP currents in particular during periods of high spiking activity. Set ``ahp_bug`` to + ``true`` to obtain this behavior in the model. + + +References +++++++++++ + +.. [1] Casti A, Hayot F, Xiao Y, Kaplan E (2008) A simple model of retina-LGN + transmission. Journal of Computational Neuroscience 24:235-252. + DOI: https://doi.org/10.1007/s10827-007-0053-7 + + +See also +++++++++ + +iaf_cond_alpha + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_th", "mV", "-45.0mV", "Threshold potential" + "E_exc", "mV", "20mV", "Excitatory reversal potential" + "E_inh", "mV", "-90mV", "Inhibitory reversal potential" + "g_L", "nS", "100nS", "Leak conductance" + "C_m", "pF", "1000.0pF", "Membrane capacitance" + "E_L", "mV", "-60.0mV", "Leak reversal Potential (aka resting potential)" + "tau_syn_exc", "ms", "1ms", "Synaptic time constant of excitatory synapse" + "tau_syn_inh", "ms", "1ms", "Synaptic time constant of inhibitory synapse" + "tau_ahp", "ms", "0.5ms", "Afterhyperpolarization (AHP) time constant" + "G_ahp", "nS", "443.8nS", "AHP conductance" + "E_ahp", "mV", "-95mV", "AHP potential" + "ahp_bug", "boolean", "false", "If true, discard AHP conductance value from previous spikes" + "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" + "V_m_prev", "mV", "E_L", "membrane potential" + "g_ahp", "nS", "0nS", "AHP conductance" + "g_ahp", "nS / ms", "0nS / ms", "AHP conductance" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ d^2 g_{ahp} } { dt^2 }= \frac{ -2 \cdot g_{ahp}' } { \tau_{ahp} } - \frac{ g_{ahp} } { { \tau_{ahp} }^{ 2 } } + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-I_{leak} - I_{syn,exc} - I_{syn,inh} - I_{ahp} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_chxk_2008_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_chxk_2008_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.919569 \ No newline at end of file diff --git a/doc/models_library/iaf_cond_alpha_neuron.rst b/doc/models_library/iaf_cond_alpha_neuron.rst new file mode 100644 index 000000000..c434c57cc --- /dev/null +++ b/doc/models_library/iaf_cond_alpha_neuron.rst @@ -0,0 +1,101 @@ +iaf_cond_alpha_neuron +##################### + + +iaf_cond_alpha - Simple conductance based leaky integrate-and-fire neuron model + +Description ++++++++++++ + +iaf_cond_alpha is an implementation of a spiking neuron using IAF dynamics with +conductance-based synapses. Incoming spike events induce a post-synaptic change +of conductance modelled by an alpha function. The alpha function +is normalised such that an event of weight 1.0 results in a peak current of 1 nS +at :math:`t = \tau_{syn}`. + + +References +++++++++++ + +.. [1] Meffin H, Burkitt AN, Grayden DB (2004). An analytical + model for the large, fluctuating synaptic conductance state typical of + neocortical neurons in vivo. Journal of Computational Neuroscience, + 16:159-175. + DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81 +.. [2] Bernander O, Douglas RJ, Martin KAC, Koch C (1991). Synaptic background + activity influences spatiotemporal integration in single pyramidal + cells. Proceedings of the National Academy of Science USA, + 88(24):11569-11573. + DOI: https://doi.org/10.1073/pnas.88.24.11569 +.. [3] Kuhn A, Rotter S (2004) Neuronal integration of synaptic input in + the fluctuation- driven regime. Journal of Neuroscience, + 24(10):2345-2356 + DOI: https://doi.org/10.1523/JNEUROSCI.3349-03.2004 + +See also +++++++++ + +iaf_cond_exp + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "250pF", "Membrane capacitance" + "g_L", "nS", "16.6667nS", "Leak conductance" + "E_L", "mV", "-70mV", "Leak reversal potential (aka resting potential)" + "refr_T", "ms", "2ms", "Duration of refractory period" + "V_th", "mV", "-55mV", "Spike threshold potential" + "V_reset", "mV", "-60mV", "Reset potential" + "E_exc", "mV", "0mV", "Excitatory reversal potential" + "E_inh", "mV", "-85mV", "Inhibitory reversal potential" + "tau_syn_exc", "ms", "0.2ms", "Synaptic time constant of excitatory synapse" + "tau_syn_inh", "ms", "2ms", "Synaptic time constant of 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" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-I_{leak} - I_{syn,exc} - I_{syn,inh} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_cond_alpha_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_cond_alpha_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.873391 \ No newline at end of file diff --git a/doc/models_library/iaf_cond_beta_neuron.rst b/doc/models_library/iaf_cond_beta_neuron.rst new file mode 100644 index 000000000..bd0e2cc1e --- /dev/null +++ b/doc/models_library/iaf_cond_beta_neuron.rst @@ -0,0 +1,117 @@ +iaf_cond_beta_neuron +#################### + + +iaf_cond_beta - Simple conductance based leaky integrate-and-fire neuron model + +Description ++++++++++++ + +iaf_cond_beta is an implementation of a spiking neuron using IAF dynamics with +conductance-based synapses. Incoming spike events induce a post-synaptic change +of conductance modelled by a beta function. The beta function +is normalised such that an event of weight 1.0 results in a peak current of +1 nS at :math:`t = \tau_{rise\_[ex|in]}`. + + +References +++++++++++ + +.. [1] Meffin H, Burkitt AN, Grayden DB (2004). An analytical + model for the large, fluctuating synaptic conductance state typical of + neocortical neurons in vivo. Journal of Computational Neuroscience, + 16:159-175. + DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81 +.. [2] Bernander O, Douglas RJ, Martin KAC, Koch C (1991). Synaptic background + activity influences spatiotemporal integration in single pyramidal + cells. Proceedings of the National Academy of Science USA, + 88(24):11569-11573. + DOI: https://doi.org/10.1073/pnas.88.24.11569 +.. [3] Kuhn A, Rotter S (2004) Neuronal integration of synaptic input in + the fluctuation- driven regime. Journal of Neuroscience, + 24(10):2345-2356 + DOI: https://doi.org/10.1523/JNEUROSCI.3349-03.2004 +.. [4] Rotter S, Diesmann M (1999). Exact simulation of time-invariant linear + systems with applications to neuronal modeling. Biologial Cybernetics + 81:381-402. + DOI: https://doi.org/10.1007/s004220050570 +.. [5] Roth A and van Rossum M (2010). Chapter 6: Modeling synapses. + in De Schutter, Computational Modeling Methods for Neuroscientists, + MIT Press. + + +See also +++++++++ + +iaf_cond_exp, iaf_cond_alpha + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "250pF", "Capacitance of the membrane" + "g_L", "nS", "16.6667nS", "Leak conductance" + "E_L", "mV", "-70mV", "Leak reversal potential (aka resting potential)" + "refr_T", "ms", "2ms", "Duration of refractory period" + "V_th", "mV", "-55mV", "Threshold potential" + "V_reset", "mV", "-60mV", "Reset potential" + "E_ex", "mV", "0mV", "Excitatory reversal potential" + "E_in", "mV", "-85mV", "Inhibitory reversal potential" + "tau_syn_rise_I", "ms", "0.2ms", "Synaptic time constant excitatory synapse" + "tau_syn_decay_I", "ms", "2ms", "Synaptic time constant for inhibitory synapse" + "tau_syn_rise_E", "ms", "0.2ms", "Synaptic time constant excitatory synapse" + "tau_syn_decay_E", "ms", "2ms", "Synaptic time constant for inhibitory synapse" + "F_E", "nS", "0nS", "Constant external input conductance (excitatory)." + "F_I", "nS", "0nS", "Constant external input conductance (inhibitory)." + "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" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "g_in", "real", "0", "inputs from the inhibitory conductance" + "g_in$", "real", "g_I_const * (1 / tau_syn_rise_I - 1 / tau_syn_decay_I)", "" + "g_ex", "real", "0", "inputs from the excitatory conductance" + "g_ex$", "real", "g_E_const * (1 / tau_syn_rise_E - 1 / tau_syn_decay_E)", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-I_{leak} - I_{syn,exc} - I_{syn,inh} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_cond_beta_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_cond_beta_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.915471 \ No newline at end of file diff --git a/doc/models_library/iaf_cond_exp_neuron.rst b/doc/models_library/iaf_cond_exp_neuron.rst new file mode 100644 index 000000000..aae637798 --- /dev/null +++ b/doc/models_library/iaf_cond_exp_neuron.rst @@ -0,0 +1,91 @@ +iaf_cond_exp_neuron +################### + + +iaf_cond_exp - Simple conductance based leaky integrate-and-fire neuron model + +Description ++++++++++++ + +iaf_cond_exp is an implementation of a spiking neuron using IAF dynamics with +conductance-based synapses. Incoming spike events induce a post-synaptic change +of conductance modelled by an exponential function. The exponential function +is normalised such that an event of weight 1.0 results in a peak conductance of +1 nS. + +References +++++++++++ + +.. [1] Meffin H, Burkitt AN, Grayden DB (2004). An analytical + model for the large, fluctuating synaptic conductance state typical of + neocortical neurons in vivo. Journal of Computational Neuroscience, + 16:159-175. + DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81 + +See also +++++++++ + +iaf_psc_delta, iaf_psc_exp, iaf_cond_exp + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "250pF", "Membrane capacitance" + "g_L", "nS", "16.6667nS", "Leak conductance" + "E_L", "mV", "-70mV", "Leak reversal potential (aka resting potential)" + "refr_T", "ms", "2ms", "Duration of refractory period" + "V_th", "mV", "-55mV", "Spike threshold potential" + "V_reset", "mV", "-60mV", "Reset potential" + "E_exc", "mV", "0mV", "Excitatory reversal potential" + "E_inh", "mV", "-85mV", "Inhibitory reversal potential" + "tau_syn_exc", "ms", "0.2ms", "Synaptic time constant of excitatory synapse" + "tau_syn_inh", "ms", "2ms", "Synaptic time constant of 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" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-I_{leak} - I_{syn,exc} - I_{syn,inh} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_cond_exp_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_cond_exp_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.876294 \ No newline at end of file diff --git a/doc/models_library/iaf_cond_exp_sfa_rr_neuron.rst b/doc/models_library/iaf_cond_exp_sfa_rr_neuron.rst new file mode 100644 index 000000000..4fe4b42f4 --- /dev/null +++ b/doc/models_library/iaf_cond_exp_sfa_rr_neuron.rst @@ -0,0 +1,113 @@ +iaf_cond_exp_sfa_rr_neuron +########################## + + +iaf_cond_exp_sfa_rr - Conductance based leaky integrate-and-fire model with spike-frequency adaptation and relative refractory mechanisms + +Description ++++++++++++ + +iaf_cond_exp_sfa_rr is an implementation of a spiking neuron using integrate-and-fire dynamics with conductance-based +synapses, with additional spike-frequency adaptation and relative refractory mechanisms as described in [2]_, page 166. + +Incoming spike events induce a post-synaptic change of conductance modelled by an exponential function. The exponential +function is normalised such that an event of weight 1.0 results in a peak current of 1 nS. + +Outgoing spike events induce a change of the adaptation and relative refractory conductances by q_sfa and q_rr, +respectively. Otherwise these conductances decay exponentially with time constants tau_sfa and tau_rr, respectively. + + +References +++++++++++ + +.. [1] Meffin H, Burkitt AN, Grayden DB (2004). An analytical + model for the large, fluctuating synaptic conductance state typical of + neocortical neurons in vivo. Journal of Computational Neuroscience, + 16:159-175. + DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81 +.. [2] Dayan P, Abbott LF (2001). Theoretical neuroscience: Computational and + mathematical modeling of neural systems. Cambridge, MA: MIT Press. + https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3006127 + + +See also +++++++++ + +aeif_cond_alpha, aeif_cond_exp, iaf_chxk_2008 + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_th", "mV", "-57.0mV", "Threshold potential" + "V_reset", "mV", "-70.0mV", "Reset potential" + "refr_T", "ms", "0.5ms", "Duration of refractory period" + "g_L", "nS", "28.95nS", "Leak conductance" + "C_m", "pF", "289.5pF", "Membrane capacitance" + "E_exc", "mV", "0mV", "Excitatory reversal potential" + "E_inh", "mV", "-75.0mV", "Inhibitory reversal potential" + "E_L", "mV", "-70.0mV", "Leak reversal potential (aka resting potential)" + "tau_syn_exc", "ms", "1.5ms", "Synaptic time constant of excitatory synapse" + "tau_syn_inh", "ms", "10.0ms", "Synaptic time constant of inhibitory synapse" + "q_sfa", "nS", "14.48nS", "Outgoing spike activated quantal spike-frequency adaptation conductance increase" + "q_rr", "nS", "3214.0nS", "Outgoing spike activated quantal relative refractory conductance increase" + "tau_sfa", "ms", "110.0ms", "Time constant of spike-frequency adaptation" + "tau_rr", "ms", "1.97ms", "Time constant of the relative refractory mechanism" + "E_sfa", "mV", "-70.0mV", "spike-frequency adaptation conductance reversal potential" + "E_rr", "mV", "-70.0mV", "relative refractory mechanism conductance reversal potential" + "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" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "g_sfa", "nS", "0nS", "inputs from the sfa conductance" + "g_rr", "nS", "0nS", "inputs from the rr conductance" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dg_{sfa} } { dt }= \frac{ -g_{sfa} } { \tau_{sfa} } + +.. math:: + \frac{ dg_{rr} } { dt }= \frac{ -g_{rr} } { \tau_{rr} } + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-I_{L} + I_{e} + I_{stim} - I_{syn,exc} - I_{syn,inh} - I_{sfa} - I_{rr}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_cond_exp_sfa_rr_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_cond_exp_sfa_rr_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.864216 \ No newline at end of file diff --git a/doc/models_library/iaf_psc_alpha_neuron.rst b/doc/models_library/iaf_psc_alpha_neuron.rst new file mode 100644 index 000000000..acfccbf9b --- /dev/null +++ b/doc/models_library/iaf_psc_alpha_neuron.rst @@ -0,0 +1,120 @@ +iaf_psc_alpha_neuron +#################### + + +iaf_psc_alpha - Leaky integrate-and-fire neuron model + +Description ++++++++++++ + +iaf_psc_alpha is an implementation of a leaky integrate-and-fire model +with alpha-function kernel synaptic currents. Thus, synaptic currents +and the resulting post-synaptic potentials have a finite rise time. + +The threshold crossing is followed by an absolute refractory period +during which the membrane potential is clamped to the resting potential. + +The general framework for the consistent formulation of systems with +neuron like dynamics interacting by point events is described in +[1]_. A flow chart can be found in [2]_. + +Critical tests for the formulation of the neuron model are the +comparisons of simulation results for different computation step +sizes. + +The iaf_psc_alpha is the standard model used to check the consistency +of the nest simulation kernel because it is at the same time complex +enough to exhibit non-trivial dynamics and simple enough compute +relevant measures analytically. + +.. note:: + If tau_m is very close to tau_syn_exc or tau_syn_inh, numerical problems + may arise due to singularities in the propagator matrics. If this is + the case, replace equal-valued parameters by a single parameter. + + For details, please see ``IAF_neurons_singularity.ipynb`` in + the NEST source code (``docs/model_details``). + + +References +++++++++++ + +.. [1] Rotter S, Diesmann M (1999). Exact simulation of + time-invariant linear systems with applications to neuronal + modeling. Biologial Cybernetics 81:381-402. + DOI: https://doi.org/10.1007/s004220050570 +.. [2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001). State + space analysis of synchronous spiking in cortical neural + networks. Neurocomputing 38-40:565-571. + DOI: https://doi.org/10.1016/S0925-2312(01)00409-X +.. [3] Morrison A, Straube S, Plesser H E, Diesmann M (2006). Exact + subthreshold integration with continuous spike times in discrete time + neural network simulations. Neural Computation, in press + DOI: https://doi.org/10.1162/neco.2007.19.1.47 + + +See also +++++++++ + +iaf_psc_delta, iaf_psc_exp, iaf_cond_alpha + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "250pF", "Capacitance of the membrane" + "tau_m", "ms", "10ms", "Membrane time constant" + "tau_syn_inh", "ms", "2ms", "Time constant of synaptic current" + "tau_syn_exc", "ms", "2ms", "Time constant of synaptic current" + "refr_T", "ms", "2ms", "Duration of refractory period" + "E_L", "mV", "-70mV", "Resting potential" + "V_reset", "mV", "-70mV", "Reset potential of the membrane" + "V_th", "mV", "-55mV", "Spike threshold potential" + "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", "" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac{ -(V_{m} - E_{L}) } { \tau_{m} } + \frac{ I } { C_{m} } + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_psc_alpha_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_psc_alpha_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.882708 \ No newline at end of file diff --git a/doc/models_library/iaf_psc_delta_neuron.rst b/doc/models_library/iaf_psc_delta_neuron.rst new file mode 100644 index 000000000..d3612aee3 --- /dev/null +++ b/doc/models_library/iaf_psc_delta_neuron.rst @@ -0,0 +1,104 @@ +iaf_psc_delta_neuron +#################### + + +iaf_psc_delta - Current-based leaky integrate-and-fire neuron model with delta-kernel post-synaptic currents + +Description ++++++++++++ + +iaf_psc_delta is an implementation of a leaky integrate-and-fire model +where the potential jumps on each spike arrival. + +The threshold crossing is followed by an absolute refractory period +during which the membrane potential is clamped to the resting potential. + +Spikes arriving while the neuron is refractory, are discarded by +default. If the property ``with_refr_input`` is set to true, such +spikes are added to the membrane potential at the end of the +refractory period, dampened according to the interval between +arrival and end of refractoriness. + +The general framework for the consistent formulation of systems with +neuron like dynamics interacting by point events is described in +[1]_. A flow chart can be found in [2]_. + + +References +++++++++++ + +.. [1] Rotter S, Diesmann M (1999). Exact simulation of + time-invariant linear systems with applications to neuronal + modeling. Biologial Cybernetics 81:381-402. + DOI: https://doi.org/10.1007/s004220050570 +.. [2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001). State + space analysis of synchronous spiking in cortical neural + networks. Neurocomputing 38-40:565-571. + DOI: https://doi.org/10.1016/S0925-2312(01)00409-X + + +See also +++++++++ + +iaf_psc_alpha, iaf_psc_exp + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "tau_m", "ms", "10ms", "Membrane time constant" + "C_m", "pF", "250pF", "Capacity of the membrane" + "refr_T", "ms", "2ms", "Duration of refractory period" + "tau_syn", "ms", "2ms", "Time constant of synaptic current" + "E_L", "mV", "-70mV", "Resting membrane potential" + "V_reset", "mV", "-70mV", "Reset potential of the membrane" + "V_th", "mV", "-55mV", "Spike threshold" + "V_min", "mV", "-inf * 1mV", "Absolute lower value for the membrane potential" + "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" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac{ -(V_{m} - E_{L}) } { \tau_{m} } + \text{convolve}(K_{\delta}, spikes) \cdot (\frac{ \mathrm{mV} } { \mathrm{ms} }) + \frac 1 { C_{m} } \left( { (I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_psc_delta_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_psc_delta_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.879017 \ No newline at end of file diff --git a/doc/models_library/iaf_psc_exp_dend_neuron.rst b/doc/models_library/iaf_psc_exp_dend_neuron.rst new file mode 100644 index 000000000..5a37e2579 --- /dev/null +++ b/doc/models_library/iaf_psc_exp_dend_neuron.rst @@ -0,0 +1,100 @@ +iaf_psc_exp_dend_neuron +####################### + + +iaf_psc_exp_dend - Leaky integrate-and-fire neuron model with exponential PSCs + +Description ++++++++++++ + +iaf_psc_exp is an implementation of a leaky integrate-and-fire model +with exponential-kernel postsynaptic currents (PSCs) according to [1]_. +Thus, postsynaptic currents have an infinitely short rise time. + +The threshold crossing is followed by an absolute refractory period (t_ref) +during which the membrane potential is clamped to the resting potential +and spiking is prohibited. + +.. note:: + If tau_m is very close to tau_syn_ex or tau_syn_in, numerical problems + may arise due to singularities in the propagator matrics. If this is + the case, replace equal-valued parameters by a single parameter. + + For details, please see ``IAF_neurons_singularity.ipynb`` in + the NEST source code (``docs/model_details``). + + +References +++++++++++ + +.. [1] Tsodyks M, Uziel A, Markram H (2000). Synchrony generation in recurrent + networks with frequency-dependent synapses. The Journal of Neuroscience, + 20,RC50:1-5. URL: https://infoscience.epfl.ch/record/183402 + + +See also +++++++++ + +iaf_cond_exp + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "250pF", "Capacity of the membrane" + "tau_m", "ms", "10ms", "Membrane time constant" + "tau_syn_inh", "ms", "2ms", "Time constant of inhibitory synaptic current" + "tau_syn_exc", "ms", "2ms", "Time constant of excitatory synaptic current" + "refr_T", "ms", "2ms", "Duration of refractory period" + "E_L", "mV", "-70mV", "Resting potential" + "V_reset", "mV", "-70mV", "Reset potential of the membrane" + "V_th", "mV", "-55mV", "Spike threshold potential" + "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" + "I_dend", "pA", "0pA", "Third factor, to be read out by synapse during weight update" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac{ -(V_{m} - E_{L}) } { \tau_{m} } + \frac 1 { C_{m} } \left( { (I_{syn} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_psc_exp_dend_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_psc_exp_dend_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.869923 \ No newline at end of file diff --git a/doc/models_library/iaf_psc_exp_htum_neuron.rst b/doc/models_library/iaf_psc_exp_htum_neuron.rst new file mode 100644 index 000000000..d0b33ed82 --- /dev/null +++ b/doc/models_library/iaf_psc_exp_htum_neuron.rst @@ -0,0 +1,115 @@ +iaf_psc_exp_htum_neuron +####################### + + +iaf_psc_exp_htum - Leaky integrate-and-fire model with separate relative and absolute refractory period + +Description ++++++++++++ + +iaf_psc_exp_htum is an implementation of a leaky integrate-and-fire model +with exponential-kernel postsynaptic currents (PSCs) according to [1]_. +The postsynaptic currents have an infinitely short rise time. +In particular, this model allows setting an absolute and relative +refractory time separately, as required by [1]_. + +The threshold crossing is followed by an absolute refractory period +(t_ref_abs) during which the membrane potential is clamped to the resting +potential. During the total refractory period (t_ref_tot), the membrane +potential evolves, but the neuron will not emit a spike, even if the +membrane potential reaches threshold. The total refractory time must be +larger or equal to the absolute refractory time. If equal, the +refractoriness of the model if equivalent to the other models of NEST. + +.. note:: + This neuron model can only be used in combination with a fixed + simulation resolution (timestep size). + +.. note:: + If tau_m is very close to tau_syn_exc or tau_syn_inh, numerical problems + may arise due to singularities in the propagator matrics. If this is + the case, replace equal-valued parameters by a single parameter. + + For details, please see ``IAF_neurons_singularity.ipynb`` in + the NEST source code (``docs/model_details``). + + +References +++++++++++ + +.. [1] Tsodyks M, Uziel A, Markram H (2000). Synchrony generation in recurrent + networks with frequency-dependent synapses. The Journal of Neuroscience, + 20,RC50:1-5. URL: https://infoscience.epfl.ch/record/183402 +.. [2] Hill, A. V. (1936). Excitation and accommodation in nerve. Proceedings of + the Royal Society of London. Series B-Biological Sciences, 119(814), 305-355. + DOI: https://doi.org/10.1098/rspb.1936.0012 +.. [3] Rotter S, Diesmann M (1999). Exact simulation of + time-invariant linear systems with applications to neuronal + modeling. Biologial Cybernetics 81:381-402. + DOI: https://doi.org/10.1007/s004220050570 +.. [4] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001). State + space analysis of synchronous spiking in cortical neural + networks. Neurocomputing 38-40:565-571. + DOI: https://doi.org/10.1016/S0925-2312(01)00409-X + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "250pF", "Capacitance of the membrane" + "tau_m", "ms", "10ms", "Membrane time constant" + "tau_syn_inh", "ms", "2ms", "Time constant of inhibitory synaptic current" + "tau_syn_exc", "ms", "2ms", "Time constant of excitatory synaptic current" + "t_ref_abs", "ms", "2ms", "Absolute refractory period" + "t_ref_tot", "ms", "2ms", "total refractory period, if t_ref_abs == t_ref_tot iaf_psc_exp_htum equivalent to iaf_psc_exp" + "E_L", "mV", "-70mV", "Resting potential" + "V_reset", "mV", "-70.0mV - E_L", "Reset value of the membrane potentia. lRELATIVE TO RESTING POTENTIAL(!) I.e. the real threshold is (V_reset + E_L)." + "V_th", "mV", "-55.0mV - E_L", "Threshold, RELATIVE TO RESTING POTENTIAL(!) I.e. the real threshold is (E_L + V_th)" + "I_e", "pA", "0pA", "constant external input current" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "r_tot", "integer", "0", "" + "r_abs", "integer", "0", "" + "V_m", "mV", "0.0mV", "Membrane potential" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac{ -V_{m} } { \tau_{m} } + \frac 1 { C_{m} } \left( { (I_{syn} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_psc_exp_htum_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_psc_exp_htum_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.785721 \ No newline at end of file diff --git a/doc/models_library/iaf_psc_exp_neuron.rst b/doc/models_library/iaf_psc_exp_neuron.rst new file mode 100644 index 000000000..89f118f6e --- /dev/null +++ b/doc/models_library/iaf_psc_exp_neuron.rst @@ -0,0 +1,124 @@ +iaf_psc_exp_neuron +################## + + +iaf_psc_exp - Leaky integrate-and-fire neuron model + +Description ++++++++++++ + +iaf_psc_exp is an implementation of a leaky integrate-and-fire model +with exponentially decaying synaptic currents according to [1]_. +Thus, postsynaptic currents have an infinitely short rise time. + +The threshold crossing is followed by an absolute refractory period +during which the membrane potential is clamped to the resting potential +and spiking is prohibited. + +The general framework for the consistent formulation of systems with +neuron like dynamics interacting by point events is described in +[1]_. A flow chart can be found in [2]_. + +Critical tests for the formulation of the neuron model are the +comparisons of simulation results for different computation step +sizes. + +.. note:: + If tau_m is very close to tau_syn_exc or tau_syn_inh, numerical problems + may arise due to singularities in the propagator matrics. If this is + the case, replace equal-valued parameters by a single parameter. + + For details, please see ``IAF_neurons_singularity.ipynb`` in + the NEST source code (``docs/model_details``). + + +References +++++++++++ + +.. [1] Rotter S, Diesmann M (1999). Exact simulation of + time-invariant linear systems with applications to neuronal + modeling. Biologial Cybernetics 81:381-402. + DOI: https://doi.org/10.1007/s004220050570 +.. [2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001). State + space analysis of synchronous spiking in cortical neural + networks. Neurocomputing 38-40:565-571. + DOI: https://doi.org/10.1016/S0925-2312(01)00409-X +.. [3] Morrison A, Straube S, Plesser H E, Diesmann M (2006). Exact + subthreshold integration with continuous spike times in discrete time + neural network simulations. Neural Computation, in press + DOI: https://doi.org/10.1162/neco.2007.19.1.47 + + +See also +++++++++ + +iaf_psc_delta, iaf_psc_alpha, iaf_cond_exp + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "250pF", "Capacitance of the membrane" + "tau_m", "ms", "10ms", "Membrane time constant" + "tau_syn_inh", "ms", "2ms", "Time constant of inhibitory synaptic current" + "tau_syn_exc", "ms", "2ms", "Time constant of excitatory synaptic current" + "refr_T", "ms", "2ms", "Duration of refractory period" + "E_L", "mV", "-70mV", "Resting potential" + "V_reset", "mV", "-70mV", "Reset value of the membrane potential" + "V_th", "mV", "-55mV", "Spike threshold potential" + "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" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "I_syn_exc", "pA", "0pA", "" + "I_syn_inh", "pA", "0pA", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dI_{syn,exc} } { dt }= \frac{ -I_{syn,exc} } { \tau_{syn,exc} } + +.. math:: + \frac{ dI_{syn,inh} } { dt }= \frac{ -I_{syn,inh} } { \tau_{syn,inh} } + +.. math:: + \frac{ dV_{m} } { dt }= \frac{ -(V_{m} - E_{L}) } { \tau_{m} } + \frac 1 { C_{m} } \left( { (I_{syn,exc} - I_{syn,inh} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `iaf_psc_exp_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: iaf_psc_exp_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.800909 \ No newline at end of file diff --git a/doc/models_library/ignore_and_fire_neuron.rst b/doc/models_library/ignore_and_fire_neuron.rst new file mode 100644 index 000000000..ac78bc10d --- /dev/null +++ b/doc/models_library/ignore_and_fire_neuron.rst @@ -0,0 +1,73 @@ +ignore_and_fire_neuron +###################### + + +ignore_and_fire - Neuron generating spikes at fixed intervals irrespective of inputs + +Description ++++++++++++ + +The ``ignore_and_fire`` neuron is a neuron model generating spikes at a predefined ``firing_rate`` with a constant inter-spike interval ("fire"), irrespective of its inputs ("ignore"). In this simplest version of the ``ignore_and_fire`` neuron, the inputs from other neurons or devices are not processed at all (*). The ``ignore_and_fire`` neuron is primarily used for neuronal-network model verification and validation purposes, in particular, to evaluate the correctness and performance of connectivity generation and inter-neuron communication. It permits an easy scaling of the network size and/or connectivity without affecting the output spike statistics. The amount of network traffic is predefined by the user, and therefore fully controllable and predictable, irrespective of the network size and structure. + +To create asynchronous activity for a population of ``ignore_and_fire`` neurons, the firing ``phase``s can be randomly initialised. Note that the firing ``phase`` is a real number, defined as the time to the next spike relative to the firing period. + +(*) The model can easily be extended and equipped with any arbitrary input processing (such as calculating input currents with alpha-function shaped PSC kernels or updating the gating variables in the Hodgkin-Huxley model) or (after-) spike generation dynamics to make it more similar and comparable to other non-ignorant neuron models. In such extended ignore_and_fire models, the spike emission process would still be decoupled from the intrinsic neuron dynamics. + +.. note:: + This neuron model can only be used in combination with a fixed + simulation resolution (timestep size). + +Authors ++++++++ + +Tetzlaff (February 2021; January 2022) + + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "firing_rate", "Bq", "10.0Bq", "firing rate" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "phase", "real", "1.0", "relative time to next spike (in (0,1])" + "phase_steps", "integer", "firing_period_steps / 2", "start halfway through the interval by default" + + + + +Equations ++++++++++ + + + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `ignore_and_fire_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: ignore_and_fire_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.810216 \ No newline at end of file diff --git a/doc/models_library/izhikevich_neuron.rst b/doc/models_library/izhikevich_neuron.rst new file mode 100644 index 000000000..1fc4f6e7d --- /dev/null +++ b/doc/models_library/izhikevich_neuron.rst @@ -0,0 +1,101 @@ +izhikevich_neuron +################# + + +izhikevich - Izhikevich neuron model + +Description ++++++++++++ + +Implementation of the simple spiking neuron model introduced by Izhikevich [1]_. The dynamics are given by: + +.. math:: + + dv/dt &= 0.04 v^2 + 5 v + 140 - u + I\\ + du/dt &= a (b v - u) + + +.. math:: + + &\text{if}\;\; v \geq V_{th}:\\ + &\;\;\;\; v \text{ is set to } c\\ + &\;\;\;\; u \text{ is incremented by } d\\ + & \, \\ + &v \text{ jumps on each spike arrival by the weight of the spike} + +Incoming spikes cause an instantaneous jump in the membrane potential proportional to the strength of the synapse. + +As published in [1]_, the numerics differs from the standard forward Euler technique in two ways: + +1) the new value of :math:`u` is calculated based on the new value of :math:`v`, rather than the previous value +2) the variable :math:`v` is updated using a time step half the size of that used to update variable :math:`u`. + +This model will instead be simulated using the numerical solver that is recommended by ODE-toolbox during code generation. + + +References +++++++++++ + +.. [1] Izhikevich, Simple Model of Spiking Neurons, IEEE Transactions on Neural Networks (2003) 14:1569-1572 + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "a", "real", "0.02", "describes time scale of recovery variable" + "b", "real", "0.2", "sensitivity of recovery variable" + "c", "mV", "-65mV", "after-spike reset value of V_m" + "d", "real", "8.0", "after-spike reset value of U_m" + "V_m_init", "mV", "-65mV", "initial membrane potential" + "V_min", "mV", "-inf * mV", "Absolute lower value for the membrane potential." + "V_th", "mV", "30mV", "Threshold potential" + "I_e", "pA", "0pA", "constant external input current" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m", "mV", "V_m_init", "Membrane potential" + "U_m", "real", "b * V_m_init", "Membrane potential recovery variable" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\frac{ 0.04 \cdot V_{m} \cdot V_{m} } { \mathrm{mV} } + 5.0 \cdot V_{m} + (140 - U_{m}) \cdot \mathrm{mV} + ((I_{e} + I_{stim}) \cdot \mathrm{GOhm})) } \right) + +.. math:: + \frac{ dU_{m} } { dt }= \frac{ a \cdot (b \cdot V_{m} - U_{m} \cdot \mathrm{mV}) } { (\mathrm{mV} \cdot \mathrm{ms}) } + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `izhikevich_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: izhikevich_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.936060 \ No newline at end of file diff --git a/doc/models_library/izhikevich_psc_alpha_neuron.rst b/doc/models_library/izhikevich_psc_alpha_neuron.rst new file mode 100644 index 000000000..a599e68cf --- /dev/null +++ b/doc/models_library/izhikevich_psc_alpha_neuron.rst @@ -0,0 +1,108 @@ +izhikevich_psc_alpha_neuron +########################### + + +izhikevich_psc_alpha - Detailed Izhikevich neuron model with alpha-kernel post-synaptic current + +Description ++++++++++++ + +Implementation of the simple spiking neuron model introduced by Izhikevich [1]_, with membrane potential in (milli)volt +and current-based synapses. + +The dynamics are given by: + +.. math:: + + C_m \frac{dV_m}{dt} = k (V - V_t)(V - V_t) - u + I + I_{syn,ex} + I_{syn,in} + \frac{dU_m}{dt} = a(b(V_m - E_L) - U_m) + + &\text{if}\;\;\; V_m \geq V_{th}:\\ + &\;\;\;\; V_m \text{ is set to } c + &\;\;\;\; U_m \text{ is incremented by } d + +On each spike arrival, the membrane potential is subject to an alpha-kernel current of the form: + +.. math:: + + I_syn = I_0 \cdot t \cdot \exp\left(-t/\tau_{syn}\right) / \tau_{syn} + +See also +++++++++ + +izhikevich, iaf_psc_alpha + + +References +++++++++++ + +.. [1] Izhikevich, Simple Model of Spiking Neurons, IEEE Transactions on Neural Networks (2003) 14:1569-1572 + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "200pF", "Membrane capacitance" + "k", "pF / (mV ms)", "8pF / mV / ms", "Spiking slope" + "V_r", "mV", "-65mV", "Resting potential" + "V_t", "mV", "-45mV", "Threshold potential" + "a", "1 / ms", "0.01 / ms", "Time scale of recovery variable" + "b", "nS", "9nS", "Sensitivity of recovery variable" + "c", "mV", "-65mV", "After-spike reset value of V_m" + "d", "pA", "60pA", "After-spike reset value of U_m" + "V_peak", "mV", "0mV", "Spike detection threshold (reset condition)" + "tau_syn_exc", "ms", "0.2ms", "Synaptic time constant of excitatory synapse" + "tau_syn_inh", "ms", "2ms", "Synaptic time constant of inhibitory synapse" + "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 + + + "V_m", "mV", "-65mV", "Membrane potential" + "U_m", "pA", "0pA", "Membrane potential recovery variable" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (k \cdot (V_{m} - V_{r}) \cdot (V_{m} - V_{t}) - U_{m} + I_{e} + I_{stim} + I_{syn,exc} - I_{syn,inh}) } \right) + +.. math:: + \frac{ dU_{m} } { dt }= a \cdot (b \cdot (V_{m} - V_{r}) - U_{m}) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `izhikevich_psc_alpha_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: izhikevich_psc_alpha_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.857486 \ No newline at end of file diff --git a/doc/models_library/mat2_psc_exp_neuron.rst b/doc/models_library/mat2_psc_exp_neuron.rst new file mode 100644 index 000000000..523c291eb --- /dev/null +++ b/doc/models_library/mat2_psc_exp_neuron.rst @@ -0,0 +1,111 @@ +mat2_psc_exp_neuron +################### + + +mat2_psc_exp - Non-resetting leaky integrate-and-fire neuron model with exponential PSCs and adaptive threshold + +Description ++++++++++++ + +mat2_psc_exp is an implementation of a leaky integrate-and-fire model +with exponential-kernel postsynaptic currents (PSCs). Thus, postsynaptic +currents have an infinitely short rise time. + +The threshold is lifted when the neuron is fired and then decreases in a +fixed time scale toward a fixed level [3]_. + +The threshold crossing is followed by a total refractory period +during which the neuron is not allowed to fire, even if the membrane +potential exceeds the threshold. The membrane potential is NOT reset, +but continuously integrated. + +.. note:: + If tau_m is very close to tau_syn_exc or tau_syn_inh, numerical problems + may arise due to singularities in the propagator matrics. If this is + the case, replace equal-valued parameters by a single parameter. + + For details, please see ``IAF_neurons_singularity.ipynb`` in + the NEST source code (``docs/model_details``). + + +References +++++++++++ + +.. [1] Rotter S and Diesmann M (1999). Exact simulation of + time-invariant linear systems with applications to neuronal + modeling. Biologial Cybernetics 81:381-402. + DOI: https://doi.org/10.1007/s004220050570 +.. [2] Diesmann M, Gewaltig M-O, Rotter S, Aertsen A (2001). State + space analysis of synchronous spiking in cortical neural + networks. Neurocomputing 38-40:565-571. + DOI:https://doi.org/10.1016/S0925-2312(01)00409-X +.. [3] Kobayashi R, Tsubo Y and Shinomoto S (2009). Made-to-order + spiking neuron model equipped with a multi-timescale adaptive + threshold. Frontiers in Computuational Neuroscience 3:9. + DOI: https://doi.org/10.3389/neuro.10.009.2009 + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "tau_m", "ms", "5ms", "Membrane time constant" + "C_m", "pF", "100pF", "Capacitance of the membrane" + "refr_T", "ms", "2ms", "Duration of refractory period" + "E_L", "mV", "-70mV", "Resting potential" + "tau_syn_exc", "ms", "1ms", "Time constant of postsynaptic excitatory currents" + "tau_syn_inh", "ms", "3ms", "Time constant of postsynaptic inhibitory currents" + "tau_1", "ms", "10ms", "Short time constant of adaptive threshold" + "tau_2", "ms", "200ms", "Long time constant of adaptive threshold" + "alpha_1", "mV", "37mV", "Amplitude of short time threshold adaption [3]" + "alpha_2", "mV", "2mV", "Amplitude of long time threshold adaption [3]" + "omega", "mV", "19mV", "Resting spike threshold (absolute value, not relative to E_L)" + "I_e", "pA", "0pA", "constant external input current" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_th_alpha_1", "mV", "0mV", "Two-timescale adaptive threshold" + "V_th_alpha_2", "mV", "0mV", "Two-timescale adaptive threshold" + "V_m", "mV", "E_L", "Absolute membrane potential." + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac{ -(V_{m} - E_{L}) } { \tau_{m} } + \frac 1 { C_{m} } \left( { (I_{syn} + I_{e} + I_{stim}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `mat2_psc_exp_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: mat2_psc_exp_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.806142 \ No newline at end of file diff --git a/doc/models_library/neuromodulated_stdp_synapse.rst b/doc/models_library/neuromodulated_stdp_synapse.rst index 9650c5427..711035f40 100644 --- a/doc/models_library/neuromodulated_stdp_synapse.rst +++ b/doc/models_library/neuromodulated_stdp_synapse.rst @@ -75,4 +75,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.345553 \ No newline at end of file + Generated at 2024-05-14 22:01:11.946258 \ No newline at end of file diff --git a/doc/models_library/noisy_synapse.rst b/doc/models_library/noisy_synapse.rst index bca1d384d..e315c2093 100644 --- a/doc/models_library/noisy_synapse.rst +++ b/doc/models_library/noisy_synapse.rst @@ -37,4 +37,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.328814 \ No newline at end of file + Generated at 2024-05-14 22:01:11.945976 \ No newline at end of file diff --git a/doc/models_library/static_synapse.rst b/doc/models_library/static_synapse.rst index 1927bfdd0..1c73514e4 100644 --- a/doc/models_library/static_synapse.rst +++ b/doc/models_library/static_synapse.rst @@ -35,4 +35,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.329827 \ No newline at end of file + Generated at 2024-05-14 22:01:11.949579 \ No newline at end of file diff --git a/doc/models_library/stdp_nn_pre_centered_synapse.rst b/doc/models_library/stdp_nn_pre_centered_synapse.rst index a7abafb84..5c49cb501 100644 --- a/doc/models_library/stdp_nn_pre_centered_synapse.rst +++ b/doc/models_library/stdp_nn_pre_centered_synapse.rst @@ -99,4 +99,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.333803 \ No newline at end of file + Generated at 2024-05-14 22:01:11.944391 \ No newline at end of file diff --git a/doc/models_library/stdp_nn_restr_symm_synapse.rst b/doc/models_library/stdp_nn_restr_symm_synapse.rst index cbbb77eb7..b30703f11 100644 --- a/doc/models_library/stdp_nn_restr_symm_synapse.rst +++ b/doc/models_library/stdp_nn_restr_symm_synapse.rst @@ -92,4 +92,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.336464 \ No newline at end of file + Generated at 2024-05-14 22:01:11.947259 \ No newline at end of file diff --git a/doc/models_library/stdp_nn_symm_synapse.rst b/doc/models_library/stdp_nn_symm_synapse.rst index 26b601728..f23a8b846 100644 --- a/doc/models_library/stdp_nn_symm_synapse.rst +++ b/doc/models_library/stdp_nn_symm_synapse.rst @@ -96,4 +96,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.341467 \ No newline at end of file + Generated at 2024-05-14 22:01:11.947977 \ No newline at end of file diff --git a/doc/models_library/stdp_synapse.rst b/doc/models_library/stdp_synapse.rst index 147e56810..46bfc0ad9 100644 --- a/doc/models_library/stdp_synapse.rst +++ b/doc/models_library/stdp_synapse.rst @@ -82,4 +82,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.343488 \ No newline at end of file + Generated at 2024-05-14 22:01:11.943540 \ No newline at end of file diff --git a/doc/models_library/stdp_triplet_synapse.rst b/doc/models_library/stdp_triplet_synapse.rst new file mode 100644 index 000000000..4c7c68fe4 --- /dev/null +++ b/doc/models_library/stdp_triplet_synapse.rst @@ -0,0 +1,72 @@ +stdp_triplet_synapse +#################### + + +stdp_triplet_synapse - Synapse type with triplet spike-timing dependent plasticity + +Description ++++++++++++ + +A connection with spike time dependent plasticity accounting for spike triplet effects (as defined in [1]_). + +Nearest-neighbour variant of pre- and postsynaptic spike coupling. + + +References +++++++++++ +.. [1] Pfister JP, Gerstner W (2006). Triplets of spikes in a model + of spike timing-dependent plasticity. The Journal of Neuroscience + 26(38):9673-9682. DOI: https://doi.org/10.1523/JNEUROSCI.1425-06.2006 + + + +Parameters +++++++++++ + + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "d", "ms", "1ms", "Synaptic transmission delay" + "tau_plus", "ms", "16.8ms", "time constant for tr_r1" + "tau_x", "ms", "101ms", "time constant for tr_r2" + "tau_minus", "ms", "33.7ms", "time constant for tr_o1" + "tau_y", "ms", "125ms", "time constant for tr_o2" + "A2_plus", "real", "7.5e-10", "" + "A3_plus", "real", "0.0093", "" + "A2_minus", "real", "0.007", "" + "A3_minus", "real", "0.00023", "" + "Wmax", "nS", "100nS", "" + "Wmin", "nS", "0nS", "" + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "w", "nS", "1nS", "Synaptic weight" + "tr_r1", "real", "0.0", "" + "tr_r2", "real", "0.0", "" + "tr_o1", "real", "0.0", "" + "tr_o2", "real", "0.0", "" +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `stdp_triplet_synapse `_. + + +Characterisation +++++++++++++++++ + +.. include:: stdp_triplet_synapse_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.948635 \ No newline at end of file diff --git a/doc/models_library/terub_gpe_neuron.rst b/doc/models_library/terub_gpe_neuron.rst new file mode 100644 index 000000000..e22775cd6 --- /dev/null +++ b/doc/models_library/terub_gpe_neuron.rst @@ -0,0 +1,117 @@ +terub_gpe_neuron +################ + + +terub_gpe - Terman Rubin neuron model + +Description ++++++++++++ + +terub_gpe is an implementation of a spiking neuron using the Terman Rubin model +based on the Hodgkin-Huxley formalism. + +(1) **Post-syaptic currents:** Incoming spike events induce a post-synaptic change of current modelled + by an alpha function. The alpha function is normalised such that an event of + weight 1.0 results in a peak current of 1 pA. + +(2) **Spike Detection:** Spike detection is done by a combined threshold-and-local-maximum search: if there + is a local maximum above a certain threshold of the membrane potential, it is considered a spike. + + +References +++++++++++ + +.. [1] Terman, D. and Rubin, J.E. and Yew, A. C. and Wilson, C.J. + Activity Patterns in a Model for the Subthalamopallidal Network + of the Basal Ganglia + The Journal of Neuroscience, 22(7), 2963-2976 (2002) + +.. [2] Rubin, J.E. and Terman, D. + High Frequency Stimulation of the Subthalamic Nucleus Eliminates + Pathological Thalamic Rhythmicity in a Computational Model + Journal of Computational Neuroscience, 16, 211-235 (2004) + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "E_L", "mV", "-55mV", "Resting membrane potential" + "g_L", "nS", "0.1nS", "Leak conductance" + "C_m", "pF", "1pF", "Capacitance of the membrane" + "E_Na", "mV", "55mV", "Sodium reversal potential" + "g_Na", "nS", "120nS", "Sodium peak conductance" + "E_K", "mV", "-80.0mV", "Potassium reversal potential" + "g_K", "nS", "30.0nS", "Potassium peak conductance" + "E_Ca", "mV", "120mV", "Calcium reversal potential" + "g_Ca", "nS", "0.15nS", "Calcium peak conductance" + "g_T", "nS", "0.5nS", "T-type Calcium channel peak conductance" + "g_ahp", "nS", "30nS", "Afterpolarization current peak conductance" + "tau_syn_exc", "ms", "1ms", "Rise time of the excitatory synaptic alpha function" + "tau_syn_inh", "ms", "12.5ms", "Rise time of the inhibitory synaptic alpha function" + "E_gg", "mV", "-100mV", "Reversal potential for inhibitory input (from GPe)" + "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 + + + "V_m", "mV", "E_L", "Membrane potential" + "V_m_old", "mV", "E_L", "Membrane potential at previous timestep for threshold check" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "gate_h", "real", "0.0", "gating variable h" + "gate_n", "real", "0.0", "gating variable n" + "gate_r", "real", "0.0", "gating variable r" + "Ca_con", "real", "0.0", "calcium concentration" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-(I_{Na} + I_{K} + I_{L} + I_{T} + I_{Ca} + I_{ahp}) \cdot \mathrm{pA} + I_{e} + I_{stim} + I_{exc,mod} \cdot \mathrm{pA} + I_{inh,mod} \cdot \mathrm{pA}) } \right) + +.. math:: + \frac{ dgate_{h} } { dt }= \frac 1 { \mathrm{ms} } \left( { g_{\phi,h} \cdot (\frac{ (h_{\infty} - gate_{h}) } { \tau_{h} }) } \right) + +.. math:: + \frac{ dgate_{n} } { dt }= \frac 1 { \mathrm{ms} } \left( { g_{\phi,n} \cdot (\frac{ (n_{\infty} - gate_{n}) } { \tau_{n} }) } \right) + +.. math:: + \frac{ dgate_{r} } { dt }= \frac 1 { \mathrm{ms} } \left( { g_{\phi,r} \cdot (\frac{ (r_{\infty} - gate_{r}) } { \tau_{r} }) } \right) + +.. math:: + \frac{ dCa_{con} } { dt }= g_{\epsilon} \cdot (-I_{Ca} - I_{T} - g_{k,Ca} \cdot Ca_{con}) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `terub_gpe_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: terub_gpe_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.885592 \ No newline at end of file diff --git a/doc/models_library/terub_stn_neuron.rst b/doc/models_library/terub_stn_neuron.rst new file mode 100644 index 000000000..47ac8f186 --- /dev/null +++ b/doc/models_library/terub_stn_neuron.rst @@ -0,0 +1,113 @@ +terub_stn_neuron +################ + + +terub_stn - Terman Rubin neuron model + +Description ++++++++++++ + +terub_stn is an implementation of a spiking neuron using the Terman Rubin model +based on the Hodgkin-Huxley formalism. + +(1) **Post-syaptic currents:** Incoming spike events induce a post-synaptic change of current modelled + by an alpha function. The alpha function is normalised such that an event of + weight 1.0 results in a peak current of 1 pA. + +(2) **Spike Detection:** Spike detection is done by a combined threshold-and-local-maximum search: if there + is a local maximum above a certain threshold of the membrane potential, it is considered a spike. + + +References +++++++++++ + +.. [1] Terman, D. and Rubin, J.E. and Yew, A.C. and Wilson, C.J. Activity Patterns in a Model for the Subthalamopallidal Network + of the Basal Ganglia. The Journal of Neuroscience, 22(7), 2963-2976 (2002) + +.. [2] Rubin, J.E. and Terman, D. High Frequency Stimulation of the Subthalamic Nucleus Eliminates + Pathological Thalamic Rhythmicity in a Computational Model Journal of Computational Neuroscience, 16, 211-235 (2004) + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "E_L", "mV", "-60mV", "Resting membrane potential" + "g_L", "nS", "2.25nS", "Leak conductance" + "C_m", "pF", "1pF", "Capacity of the membrane" + "E_Na", "mV", "55mV", "Sodium reversal potential" + "g_Na", "nS", "37.5nS", "Sodium peak conductance" + "E_K", "mV", "-80mV", "Potassium reversal potential" + "g_K", "nS", "45nS", "Potassium peak conductance" + "E_Ca", "mV", "140mV", "Calcium reversal potential" + "g_Ca", "nS", "0.5nS", "Calcium peak conductance" + "g_T", "nS", "0.5nS", "T-type Calcium channel peak conductance" + "g_ahp", "nS", "9nS", "Afterpolarization current peak conductance" + "tau_syn_exc", "ms", "1ms", "Rise time of the excitatory synaptic alpha function" + "tau_syn_inh", "ms", "0.08ms", "Rise time of the inhibitory synaptic alpha function" + "E_gs", "mV", "-85mV", "Reversal potential for inhibitory input (from GPe)" + "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 + + + "V_m", "mV", "E_L", "Membrane potential" + "V_m_old", "mV", "E_L", "Membrane potential at previous timestep for threshold check" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "gate_h", "real", "0.0", "gating variable h" + "gate_n", "real", "0.0", "gating variable n" + "gate_r", "real", "0.0", "gating variable r" + "Ca_con", "real", "0.0", "calcium concentration" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-(I_{Na} + I_{K} + I_{L} + I_{T} + I_{Ca} + I_{ahp}) + I_{e} + I_{stim} + I_{exc,mod} + I_{inh,mod}) } \right) + +.. math:: + \frac{ dgate_{h} } { dt }= \phi_{h} \cdot (\frac{ (h_{\infty} - gate_{h}) } { \tau_{h} }) + +.. math:: + \frac{ dgate_{n} } { dt }= \phi_{n} \cdot (\frac{ (n_{\infty} - gate_{n}) } { \tau_{n} }) + +.. math:: + \frac{ dgate_{r} } { dt }= \phi_{r} \cdot (\frac{ (r_{\infty} - gate_{r}) } { \tau_{r} }) + +.. math:: + \frac{ dCa_{con} } { dt }= \epsilon \cdot (\frac{ (-I_{Ca} - I_{T}) } { \mathrm{pA} } - k_{Ca} \cdot Ca_{con}) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `terub_stn_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: terub_stn_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.788939 \ No newline at end of file diff --git a/doc/models_library/third_factor_stdp_synapse.rst b/doc/models_library/third_factor_stdp_synapse.rst index 19784b10b..a34d3cfbb 100644 --- a/doc/models_library/third_factor_stdp_synapse.rst +++ b/doc/models_library/third_factor_stdp_synapse.rst @@ -83,4 +83,4 @@ Characterisation .. footer:: - Generated at 2023-11-16 11:40:54.330550 \ No newline at end of file + Generated at 2024-05-14 22:01:11.945146 \ No newline at end of file diff --git a/doc/models_library/traub_cond_multisyn_neuron.rst b/doc/models_library/traub_cond_multisyn_neuron.rst new file mode 100644 index 000000000..a6cb32906 --- /dev/null +++ b/doc/models_library/traub_cond_multisyn_neuron.rst @@ -0,0 +1,129 @@ +traub_cond_multisyn_neuron +########################## + + +traub_cond_multisyn - Traub model according to Borgers 2017 + +Description ++++++++++++ + +Reduced Traub-Miles Model of a Pyramidal Neuron in Rat Hippocampus [1]_. +parameters got from reference [2]_ chapter 5. + +AMPA, NMDA, GABA_A, and GABA_B conductance-based synapses with +beta-function (difference of two exponentials) time course corresponding +to "hill_tononi" model. + + +References +++++++++++ + +.. [1] R. D. Traub and R. Miles, Neuronal Networks of the Hippocampus,Cam- bridge University Press, Cambridge, UK, 1991. +.. [2] Borgers, C., 2017. An introduction to modeling neuronal dynamics (Vol. 66). Cham: Springer. + + +See also +++++++++ + +hh_cond_exp_traub + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "100.0pF", "Membrane Capacitance" + "g_Na", "nS", "10000.0nS", "Sodium peak conductance" + "g_K", "nS", "8000.0nS", "Potassium peak conductance" + "g_L", "nS", "10nS", "Leak conductance" + "E_Na", "mV", "50.0mV", "Sodium reversal potential" + "E_K", "mV", "-100.0mV", "Potassium reversal potentia" + "E_L", "mV", "-67.0mV", "Leak reversal Potential (aka resting potential)" + "V_Tr", "mV", "-20.0mV", "Spike Threshold" + "refr_T", "ms", "2ms", "Duration of refractory period" + "AMPA_g_peak", "nS", "0.1nS", "Parameters for synapse of type AMPA, GABA_A, GABA_B and NMDApeak conductance" + "AMPA_E_rev", "mV", "0.0mV", "reversal potential" + "tau_AMPA_1", "ms", "0.5ms", "rise time" + "tau_AMPA_2", "ms", "2.4ms", "decay time, Tau_1 < Tau_2" + "NMDA_g_peak", "nS", "0.075nS", "peak conductance" + "tau_NMDA_1", "ms", "4.0ms", "rise time" + "tau_NMDA_2", "ms", "40.0ms", "decay time, Tau_1 < Tau_2" + "NMDA_E_rev", "mV", "0.0mV", "reversal potential" + "NMDA_Vact", "mV", "-58.0mV", "inactive for V << Vact, inflection of sigmoid" + "NMDA_Sact", "mV", "2.5mV", "scale of inactivation" + "GABA_A_g_peak", "nS", "0.33nS", "peak conductance" + "tau_GABAA_1", "ms", "1.0ms", "rise time" + "tau_GABAA_2", "ms", "7.0ms", "decay time, Tau_1 < Tau_2" + "GABA_A_E_rev", "mV", "-70.0mV", "reversal potential" + "GABA_B_g_peak", "nS", "0.0132nS", "peak conductance" + "tau_GABAB_1", "ms", "60.0ms", "rise time" + "tau_GABAB_2", "ms", "200.0ms", "decay time, Tau_1 < Tau_2" + "GABA_B_E_rev", "mV", "-90.0mV", "reversal potential for intrinsic current" + "I_e", "pA", "0pA", "constant external input current" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m", "mV", "-70.0mV", "Membrane potential" + "V_m_old", "mV", "E_L", "Membrane potential at previous timestep for threshold check" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "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" + "g_AMPA", "real", "0", "" + "g_NMDA", "real", "0", "" + "g_GABAA", "real", "0", "" + "g_GABAB", "real", "0", "" + "g_AMPA$", "real", "AMPAInitialValue", "" + "g_NMDA$", "real", "NMDAInitialValue", "" + "g_GABAA$", "real", "GABA_AInitialValue", "" + "g_GABAB$", "real", "GABA_BInitialValue", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-(I_{Na} + I_{K} + I_{L}) + I_{e} + I_{stim} + I_{syn}) } \right) + +.. math:: + \frac{ dAct_{n} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{n} \cdot (1 - Act_{n}) - \beta_{n} \cdot Act_{n}) } \right) + +.. math:: + \frac{ dAct_{m} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{m} \cdot (1 - Act_{m}) - \beta_{m} \cdot Act_{m}) } \right) + +.. math:: + \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_cond_multisyn_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: traub_cond_multisyn_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.754988 \ No newline at end of file diff --git a/doc/models_library/traub_psc_alpha_neuron.rst b/doc/models_library/traub_psc_alpha_neuron.rst new file mode 100644 index 000000000..d3331897a --- /dev/null +++ b/doc/models_library/traub_psc_alpha_neuron.rst @@ -0,0 +1,101 @@ +traub_psc_alpha_neuron +###################### + + +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 +++++++++++ + +.. [1] R. D. Traub and R. Miles, Neuronal Networks of the Hippocampus,Cam- bridge University Press, Cambridge, UK, 1991. +.. [2] Borgers, C., 2017. An introduction to modeling neuronal dynamics (Vol. 66). Cham: Springer. + + +See also +++++++++ + +hh_cond_exp_traub + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m_init", "mV", "-70mV", "Initial membrane potential" + "C_m", "pF", "100pF", "Membrane capacitance" + "g_Na", "nS", "10000nS", "Sodium peak conductance" + "g_K", "nS", "8000nS", "Potassium peak conductance" + "g_L", "nS", "10nS", "Leak conductance" + "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" + "refr_T", "ms", "2ms", "Duration of refractory period" + "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 ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m", "mV", "V_m_init", "Membrane potential" + "V_m_old", "mV", "V_m_init", "Membrane potential at previous timestep for threshold check" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "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 ++++++++++ + + + +.. math:: + \frac{ dAct_{n} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{n} \cdot (1 - Act_{n}) - \beta_{n} \cdot Act_{n}) } \right) + +.. math:: + \frac{ dAct_{m} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{m} \cdot (1 - Act_{m}) - \beta_{m} \cdot Act_{m}) } \right) + +.. math:: + \frac{ dInact_{h} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\alpha_{h} \cdot (1 - Inact_{h}) - \beta_{h} \cdot Inact_{h}) } \right) + +.. math:: + \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) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `traub_psc_alpha_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: traub_psc_alpha_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.847233 \ No newline at end of file diff --git a/doc/models_library/wb_cond_exp_neuron.rst b/doc/models_library/wb_cond_exp_neuron.rst new file mode 100644 index 000000000..0c39321cb --- /dev/null +++ b/doc/models_library/wb_cond_exp_neuron.rst @@ -0,0 +1,104 @@ +wb_cond_exp_neuron +################## + + +wb_cond_exp - Wang-Buzsaki model + +Description ++++++++++++ + +wb_cond_exp is an implementation of a modified Hodkin-Huxley model. + +(1) Post-synaptic currents: Incoming spike events induce a post-synaptic change + of conductance modeled by an exponential function. + +(2) Spike Detection: Spike detection is done by a combined threshold-and-local- + maximum search: if there is a local maximum above a certain threshold of + the membrane potential, it is considered a spike. + +References +++++++++++ + +.. [1] Wang, X.J. and Buzsaki, G., (1996) Gamma oscillation by synaptic + inhibition in a hippocampal interneuronal network model. Journal of + neuroscience, 16(20), pp.6402-6413. + +See Also +++++++++ + +hh_cond_exp_traub, wb_cond_multisyn + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "C_m", "pF", "100pF", "Membrane capacitance" + "g_Na", "nS", "3500nS", "Sodium peak conductance" + "g_K", "nS", "900nS", "Potassium peak conductance" + "g_L", "nS", "10nS", "Leak conductance" + "E_Na", "mV", "55mV", "Sodium reversal potential" + "E_K", "mV", "-90mV", "Potassium reversal potential" + "E_L", "mV", "-65mV", "Leak reversal potential (aka resting potential)" + "V_Tr", "mV", "-55mV", "Spike threshold" + "refr_T", "ms", "2ms", "Duration of refractory period" + "tau_syn_exc", "ms", "0.2ms", "Rise time of the excitatory synaptic alpha function" + "tau_syn_inh", "ms", "10ms", "Rise time of the inhibitory synaptic alpha function" + "E_exc", "mV", "0mV", "Excitatory synaptic reversal potential" + "E_inh", "mV", "-75mV", "Inhibitory synaptic reversal potential" + "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" + "V_m_old", "mV", "E_L", "Membrane potential at previous timestep for threshold check" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "Inact_h", "real", "alpha_h_init / (alpha_h_init + beta_h_init)", "" + "Act_n", "real", "alpha_n_init / (alpha_n_init + beta_n_init)", "" + + + + +Equations ++++++++++ + + + +.. math:: + \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) + +.. math:: + \frac{ dAct_{n} } { dt }= (\text{alpha_n}(V_{m}) \cdot (1 - Act_{n}) - \text{beta_n}(V_{m}) \cdot Act_{n}) + +.. math:: + \frac{ dInact_{h} } { dt }= (\text{alpha_h}(V_{m}) \cdot (1 - Inact_{h}) - \text{beta_h}(V_{m}) \cdot Inact_{h}) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `wb_cond_exp_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: wb_cond_exp_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.747965 \ No newline at end of file diff --git a/doc/models_library/wb_cond_multisyn_neuron.rst b/doc/models_library/wb_cond_multisyn_neuron.rst new file mode 100644 index 000000000..81cf1f1f1 --- /dev/null +++ b/doc/models_library/wb_cond_multisyn_neuron.rst @@ -0,0 +1,126 @@ +wb_cond_multisyn_neuron +####################### + + +wb_cond_multisyn - Wang-Buzsaki model with multiple synapses + +Description ++++++++++++ + +wb_cond_multisyn is an implementation of a modified Hodkin-Huxley model. + +Spike detection is done by a combined threshold-and-local-maximum search: if +there is a local maximum above a certain threshold of the membrane potential, +it is considered a spike. + +AMPA, NMDA, GABA_A, and GABA_B conductance-based synapses with +beta-function (difference of two exponentials) time course. + +References +++++++++++ + +.. [1] Wang, X.J. and Buzsaki, G., (1996) Gamma oscillation by synaptic + inhibition in a hippocampal interneuronal network model. Journal of + Neuroscience, 16(20), pp.6402-6413. + +See also +++++++++ + +wb_cond_multisyn + + + +Parameters +++++++++++ +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "g_Na", "nS", "3500.0nS", "Sodium peak conductance" + "g_K", "nS", "900.0nS", "Potassium peak conductance" + "g_L", "nS", "10nS", "Leak conductance" + "C_m", "pF", "100.0pF", "Membrane Capacitance" + "E_Na", "mV", "55.0mV", "Sodium reversal potential" + "E_K", "mV", "-90.0mV", "Potassium reversal potentia" + "E_L", "mV", "-65.0mV", "Leak reversal Potential (aka resting potential)" + "V_Tr", "mV", "-55.0mV", "Spike Threshold" + "refr_T", "ms", "2ms", "Duration of refractory period" + "AMPA_g_peak", "nS", "0.1nS", "Parameters for synapse of type AMPA, GABA_A, GABA_B and NMDApeak conductance" + "AMPA_E_rev", "mV", "0.0mV", "reversal potential" + "AMPA_Tau_1", "ms", "0.5ms", "rise time" + "AMPA_Tau_2", "ms", "2.4ms", "decay time, Tau_1 < Tau_2" + "NMDA_g_peak", "nS", "0.075nS", "peak conductance" + "NMDA_Tau_1", "ms", "4.0ms", "rise time" + "NMDA_Tau_2", "ms", "40.0ms", "decay time, Tau_1 < Tau_2" + "NMDA_E_rev", "mV", "0.0mV", "reversal potential" + "NMDA_Vact", "mV", "-58.0mV", "inactive for V << Vact, inflection of sigmoid" + "NMDA_Sact", "mV", "2.5mV", "scale of inactivation" + "GABA_A_g_peak", "nS", "0.33nS", "peak conductance" + "GABA_A_Tau_1", "ms", "1.0ms", "rise time" + "GABA_A_Tau_2", "ms", "7.0ms", "decay time, Tau_1 < Tau_2" + "GABA_A_E_rev", "mV", "-70.0mV", "reversal potential" + "GABA_B_g_peak", "nS", "0.0132nS", "peak conductance" + "GABA_B_Tau_1", "ms", "60.0ms", "rise time" + "GABA_B_Tau_2", "ms", "200.0ms", "decay time, Tau_1 < Tau_2" + "GABA_B_E_rev", "mV", "-90.0mV", "reversal potential for intrinsic current" + "I_e", "pA", "0pA", "constant external input current" + + + +State variables ++++++++++++++++ + +.. csv-table:: + :header: "Name", "Physical unit", "Default value", "Description" + :widths: auto + + + "V_m", "mV", "-65.0mV", "Membrane potential" + "V_m_old", "mV", "E_L", "Membrane potential at previous timestep for threshold check" + "refr_t", "ms", "0ms", "Refractory period timer" + "is_refractory", "boolean", "false", "" + "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" + "g_AMPA", "real", "0", "" + "g_NMDA", "real", "0", "" + "g_GABAA", "real", "0", "" + "g_GABAB", "real", "0", "" + "g_AMPA$", "real", "AMPAInitialValue", "" + "g_NMDA$", "real", "NMDAInitialValue", "" + "g_GABAA$", "real", "GABA_AInitialValue", "" + "g_GABAB$", "real", "GABA_BInitialValue", "" + + + + +Equations ++++++++++ + + + +.. math:: + \frac{ dInact_{h} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\text{alpha_h}(V_{m}) \cdot (1 - Inact_{h}) - \text{beta_h}(V_{m}) \cdot Inact_{h}) } \right) + +.. math:: + \frac{ dAct_{n} } { dt }= \frac 1 { \mathrm{ms} } \left( { (\text{alpha_n}(V_{m}) \cdot (1 - Act_{n}) - \text{beta_n}(V_{m}) \cdot Act_{n}) } \right) + +.. math:: + \frac{ dV_{m} } { dt }= \frac 1 { C_{m} } \left( { (-(I_{Na} + I_{K} + I_{L}) + I_{e} + I_{stim} + I_{syn}) } \right) + + + +Source code ++++++++++++ + +The model source code can be found in the NESTML models repository here: `wb_cond_multisyn_neuron `_. + +Characterisation +++++++++++++++++ + +.. include:: wb_cond_multisyn_neuron_characterisation.rst + + +.. footer:: + + Generated at 2024-05-14 22:01:11.835023 \ No newline at end of file diff --git a/pynestml/codegeneration/autodoc_code_generator.py b/pynestml/codegeneration/autodoc_code_generator.py index 9f1c05fc6..c07770525 100644 --- a/pynestml/codegeneration/autodoc_code_generator.py +++ b/pynestml/codegeneration/autodoc_code_generator.py @@ -38,7 +38,6 @@ from pynestml.meta_model.ast_model import ASTModel from pynestml.utils.ast_utils import ASTUtils from pynestml.utils.logger import Logger -from pynestml.utils.string_utils import removesuffix class AutoDocCodeGenerator(CodeGenerator): @@ -89,7 +88,7 @@ def generate_neuron_code(self, neuron: ASTModel): :param neuron: a single neuron object. """ nestml_model_doc = self._template_neuron_nestml_model.render(self.setup_neuron_model_generation_helpers(neuron)) - neuron_name = removesuffix(neuron.get_name(), "_neuron") + neuron_name = neuron.get_name() with open(str(os.path.join(FrontendConfiguration.get_target_path(), neuron_name)) + '.rst', 'w+') as f: f.write(str(nestml_model_doc)) @@ -116,7 +115,7 @@ def setup_neuron_model_generation_helpers(self, neuron: ASTModel): namespace['now'] = datetime.datetime.utcnow() namespace['neuron'] = neuron - namespace['neuronName'] = removesuffix(str(neuron.get_name()), "_neuron") + namespace['neuronName'] = neuron.get_name() namespace['printer'] = self._printer namespace['assignments'] = NestAssignmentsHelper() namespace['utils'] = ASTUtils()