Merging opt tutorials from 2024 bmtk-workshop

- Updated format to match main bmtk tutorials
- Fixed typos

docs/tutorial/01_opt_external_models/Purkinje_morpho_1_number.py

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+number_ind_1 = dict()
+number_ind_1['indiv'] = 138
+number_ind_1['morfo_n'] = 1
+
+
+

docs/tutorial/01_opt_external_models/Synapses.py

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+from neuron import h
+
+class Synapse_py3:
+    def __init__(self,source,target,section,weight = 1):
+
+        self.input = h.NetStim(0.5)
+        self.input.start = -10
+        self.input.number = 1
+        self.input.interval = 1e9
+        self.weight = weight
+
+
+        self.postsyns = {}
+
+        if (type(source) == type('s')):
+            sourcetype = source
+
+
+#Purkinje cell
+        if sourcetype == 'pf':
+            if target.whatami == 'prk':
+                # Make a PF synapse onto a purkinje cell
+                # Use deterministic synapses*
+                self.whatami = "syn_pf2prk_det"
+                self.postsyns['AMPA'] = [h.PF_syn(0.5, sec=section)]
+                self.postsyns['AMPA'][0].tau_facil=10.8*5#
+                self.postsyns['AMPA'][0].tau_rec=35.1 
+                self.postsyns['AMPA'][0].tau_1=3*2
+                self.postsyns['AMPA'][0].gmax = 2800
+                self.postsyns['AMPA'][0].U=0.13
+
+                self.nc_syn = [h.NetCon(self.input,receptor[0],0,0.1,1) for receptor in self.postsyns.values()]
+
+        elif sourcetype == 'aa':
+            if target.whatami == 'prk':
+                # Make a ascending axon synapse onto a purkinje cell
+                # Use deterministic synapses
+                self.whatami = "syn_aa2prk_det"
+                self.postsyns['AMPA'] = [h.PF_syn(0.5, sec=section)]
+                self.postsyns['AMPA'][0].tau_facil=10.8*5
+                self.postsyns['AMPA'][0].tau_rec=35.1*1
+                self.postsyns['AMPA'][0].tau_1=3*5
+                self.postsyns['AMPA'][0].gmax = 2800
+                self.postsyns['AMPA'][0].U=0.13
+
+                self.nc_syn = [h.NetCon(self.input,receptor[0],0,0.1,1) for receptor in self.postsyns.values()]
+
+        elif sourcetype == 'stl':
+            if target.whatami == 'prk':
+                self.whatami = "syn_stl2prk_alpha1"
+                self.postsyns['GABA'] = [h.PC_gaba_alpha1(0.5, sec=section)] # self.postsyns
+                self.postsyns['GABA'][0].tau_facil=4
+                self.postsyns['GABA'][0].tau_rec=15
+                self.postsyns['GABA'][0].tau_1=1
+                self.postsyns['GABA'][0].Erev = -60
+                self.postsyns['GABA'][0].gmaxA1 = 2600
+                self.postsyns['GABA'][0].U=0.35
+                self.nc_syn = [h.NetCon(self.input,receptor[0],0,0.1,1) for receptor in self.postsyns.values()] 	
+
+        else:
+            print('SOURCE TYPE DOES NOT EXIST SOMETHING WRONG!!!!!!!!!')
+
+

docs/tutorial/01_opt_external_models/build_network.L5_updated.py

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+from bmtk.builder.networks import NetworkBuilder
+
+net = NetworkBuilder('L5')
+net.add_nodes(
+    N=5,
+    model_type='biophysical',
+    model_template='hoc:L5PCtemplate',
+    morphology='cell3.asc'
+)
+
+net.add_nodes(
+    N=5,
+    model_type='biophysical',
+    model_template='hoc:HNTemplate',
+    morphology='HL5PN1.swc'
+)
+
+
+net.build()
+net.save(output_dir='network_L5_updated')
+
+
+
+virt_exc = NetworkBuilder('virt_exc')
+virt_exc.add_nodes(
+    N=20,
+    model_type='virtual',
+    ei_type='exc'
+)
+conns = virt_exc.add_edges(
+    source=virt_exc.nodes(),
+    target=net.nodes(),
+    connection_rule=12,
+    model_template='Exp2Syn',
+    dynamics_params='AMPA_ExcToExc.json',
+    distance_range=[0.0, 1.0e20],
+    target_sections=['soma', 'basal', 'apical'],
+    delay=2.0,
+    syn_weight=0.01
+)
+
+virt_exc.build()
+virt_exc.save(output_dir='network_L5_updated')

docs/tutorial/01_opt_external_models/build_network.added_channels.py

@@ -0,0 +1,36 @@
+from bmtk.builder.networks import NetworkBuilder
+
+net = NetworkBuilder('L5')
+net.add_nodes(
+    N=1,
+    model_type='biophysical',
+    model_template='hoc:L5PCtemplate',
+    morphology='cell3.asc',
+    model_processing='add_channels'
+)
+
+
+net.build()
+net.save(output_dir='network_added_channels')
+
+
+virt_exc = NetworkBuilder('virt_exc')
+virt_exc.add_nodes(
+    N=20,
+    model_type='virtual',
+    ei_type='exc'
+)
+conns = virt_exc.add_edges(
+    source=virt_exc.nodes(),
+    target=net.nodes(),
+    connection_rule=12,
+    model_template='Exp2Syn',
+    dynamics_params='AMPA_ExcToExc.json',
+    distance_range=[0.0, 1.0e20],
+    target_sections=['soma', 'basal', 'apical'],
+    delay=2.0,
+    syn_weight=0.01
+)
+
+virt_exc.build()
+virt_exc.save(output_dir='network_added_channels')

docs/tutorial/01_opt_external_models/build_network.ball_and_stick.py

@@ -0,0 +1,42 @@
+import numpy as np
+from bmtk.builder.networks import NetworkBuilder
+
+
+net = NetworkBuilder('BAS')
+net.add_nodes(
+    N=5,
+    model_type='biophysical',
+    model_template='python:loadBAS',
+    gnabar=np.random.uniform(0.10, 0.15, size=5),
+    gkbar=np.random.uniform(0.02, 0.04, size=5),
+    gl=np.random.uniform(0.0001, 0.0005, size=5),
+    el=np.random.uniform(-80.0, -50.0, size=5),
+    g_pas = 0.001,
+    e_pas = -65
+)
+
+
+net.build()
+net.save(output_dir='network_BAS')
+
+
+virt_exc = NetworkBuilder('virt_exc')
+virt_exc.add_nodes(
+    N=20,
+    model_type='virtual',
+    ei_type='exc'
+)
+conns = virt_exc.add_edges(
+    source=virt_exc.nodes(),
+    target=net.nodes(),
+    connection_rule=12,
+    model_template='Exp2Syn',
+    dynamics_params='AMPA_ExcToExc.json',
+    distance_range=[0.0, 1.0e20],
+    target_sections=['soma', 'dend'],
+    delay=1.0,
+    syn_weight=0.05
+)
+
+virt_exc.build()
+virt_exc.save(output_dir='network_BAS')

docs/tutorial/01_opt_external_models/build_network.modified_densities.py

@@ -0,0 +1,36 @@
+from bmtk.builder.networks import NetworkBuilder
+
+net = NetworkBuilder('L5')
+net.add_nodes(
+    N=1,
+    model_type='biophysical',
+    model_template='hoc:L5PCtemplate',
+    morphology='cell3.asc',
+    model_processing='adjust_densities'
+)
+
+
+net.build()
+net.save(output_dir='network_modified_densities')
+
+
+virt_exc = NetworkBuilder('virt_exc')
+virt_exc.add_nodes(
+    N=20,
+    model_type='virtual',
+    ei_type='exc'
+)
+conns = virt_exc.add_edges(
+    source=virt_exc.nodes(),
+    target=net.nodes(),
+    connection_rule=12,
+    model_template='Exp2Syn',
+    dynamics_params='AMPA_ExcToExc.json',
+    distance_range=[0.0, 1.0e20],
+    target_sections=['soma', 'basal', 'apical'],
+    delay=2.0,
+    syn_weight=0.01
+)
+
+virt_exc.build()
+virt_exc.save(output_dir='network_modified_densities')

docs/tutorial/01_opt_external_models/build_network.purkinje.py

@@ -0,0 +1,45 @@
+import os
+import numpy as np
+
+from bmtk.builder.networks import NetworkBuilder
+
+
+np.random.seed(100)
+
+net = NetworkBuilder('purkinje')
+net.add_nodes( 
+    pop_name='Scnn1a',
+    model_type='biophysical',
+    model_template='python:Purkinje_morph_1',
+    spines_on=0
+)
+net.add_nodes( 
+    pop_name='Scnn1a',
+    model_type='biophysical',
+    model_template='python:Purkinje_morph_1',
+    spines_on=1
+)
+
+
+net.build()
+net.save(output_dir='network_Purkinje')
+
+
+virt = NetworkBuilder('virt_exc')
+virt.add_nodes(
+    N=5,
+    model_type='virtual'
+)
+virt.add_edges(
+    source=virt.nodes(), target=net.nodes(),
+    connection_rule=12,
+    syn_weight=6.4e-05,
+    distance_range=[50.0, 10.0e20],
+    target_sections=['dend'],
+    delay=1.0,
+    dynamics_params='AMPA_ExcToExc.json',
+    model_template='exp2syn'
+)
+
+virt.build()
+virt.save(output_dir='network_Purkinje')

docs/tutorial/01_opt_external_models/components/dLGN_mechanisms/modfiles/iahp.mod

@@ -0,0 +1,96 @@
+TITLE Medium duration Ca-dependent potassium current
+:
+:   Ca++ dependent K+ current IC responsible for medium duration AHP
+:
+:   Original file written by Alain Destexhe, Salk Institute, Nov 3, 1992
+:   Modified by Geir Halnes, Norwegian University of Life Sciences, Mar 13, 2011
+
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	SUFFIX iahp
+	USEION k READ ek WRITE ik VALENCE 1
+	USEION Ca READ Cai VALENCE 2
+      RANGE gkbar, g, minf, taum
+	GLOBAL beta, cac, m_inf, tau_m, x
+}
+
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(molar) = (1/liter)
+	(mM) = (millimolar)
+}
+
+
+PARAMETER {
+	v (mV)
+	ek = -90 (mV)
+	celsius = 36 (degC)
+	Cai 	= 5e-5 (mM)			: Initial [Ca]i = 50 nM (Cai is simulated by separate mod-file)
+	gkbar	= 1.3e-4	(mho/cm2)	: Conductance (modified from hoc-file)
+	beta	= 0.02	(1/ms)	: Backward rate constant
+	cac	= 4.3478e-4(mM)		: Middle point of m_inf fcn
+	taumin	= 1	(ms)		: Minimal value of the time cst
+      x       = 2				: Binding cites
+}
+
+
+
+
+STATE {
+	m
+}
+
+
+ASSIGNED {
+	ik 	(mA/cm2)
+	g       (mho/cm2)
+	m_inf
+	tau_m	(ms)
+	minf
+      taum
+	tadj
+}
+
+
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+        minf = m_inf
+        taum = tau_m
+	  g = gkbar*m*m
+	  ik = g * (v - ek)
+}
+
+DERIVATIVE states { 
+	evaluate_fct(v,Cai)
+	m' = (m_inf - m) / tau_m
+}
+
+
+UNITSOFF
+INITIAL {
+:  activation kinetics are assumed to be at 22 deg. C
+:  Q10 is assumed to be 3
+
+	VERBATIM
+	Cai = _ion_Cai;
+	ENDVERBATIM
+
+	tadj = 3 ^ ((celsius-22.0)/10)
+	evaluate_fct(v,Cai)
+	m = m_inf
+      minf = m_inf
+      taum = tau_m
+}
+
+PROCEDURE evaluate_fct(v(mV),Cai(mM)) {  LOCAL car, tcar
+	car = (Cai/cac)^x
+	m_inf = car / ( 1 + car )
+	tau_m = 1 / beta / (1 + car) / tadj
+      if(tau_m < taumin) { tau_m = taumin } 	: min value of time cst
+}
+
+UNITSON