#Protocols for fitting the non-synaptic conductances in four steps.
#Defines which quantities are fitted at each step, what stimuli are for each objective function, etc.
#
#Tuomo Maki-Marttunen, 2015-2016
#
from neuron import h
#Names and ranges of each varied model parameter. Grouped into four fitting steps.
snmf_variables = [ [ ["L_soma", 11.58, 46.34], #First step parameters: L_soma
["L_dend", 141.06, 564.26], #L_basal
["L_apic[0]", 325.0, 1300.0], #L_apical
["L_apic[1]", 325.26, 1301.06], #L_apical2
["Ra_soma", 20, 500], #Ra_soma
["Ra_dend", 10, 1000], #Ra_basal
["Ra_apic[0]", 10, 1000], #Ra_apical
["Ra_apic[1]", 10, 1000], #Ra_apical2
["cm_soma", 0.5, 2.0], #Cm_soma
["cm_dend", 0.5, 4.0], #Cm_basal
["cm_apic[0]", 0.5, 4.0], #Cm_apical
["cm_apic[1]", 0.5, 4.0], #Cm_apical2
["g_pas_soma", 0.00002, 0.0001], #gpas_soma
["g_pas_dend", 0.000015, 0.0001], #gpas_basal
["g_pas_apic[0]", 0.000015, 0.0001], #gpas_apical
["g_pas_apic[1]", 0.000015, 0.0001] ], #gpas_apical2
[ ["ehcn_Ih_*", -55, -35], #Second step parameters: E_h (Not fitted in Table 2. Used +-10 mV)
["gIhbar_Ih_soma", 0, 0.0008], #Ih_soma (Not fitted in Table 2. Used 4-fold wrt Hay value)
["gIhbar_Ih_dend", 0, 0.0008], #Ih_basal (Not fitted in Table 2. Used 4-fold wrt Hay value)
["gIhbar_Ih_apic[0]", 0, 0.008], #Ih_apical (Not fitted in Table 2. Used 8-fold wrt mean Hay value)
["gIhbar_Ih_apic[1]", 0, 0.016], #Ih_apical2 (Not fitted in Table 2. Used 16-fold wrt mean Hay value)
["g_pas_soma", 0.00002, 0.0001], #gpas_soma
["g_pas_dend", 0.000015, 0.0001], #gpas_basal
["g_pas_apic[0]", 0.000015, 0.0001], #gpas_apical
["g_pas_apic[1]", 0.000015, 0.0001] ], #gpas_apical2
[ ["gCa_HVAbar_Ca_HVA_soma", 0, 0.001], #CaHVA_soma
["gCa_LVAstbar_Ca_LVAst_soma", 0, 0.01], #CaLVA_soma
["gamma_CaDynamics_E2_soma", 0.0005, 0.05], #gamma_soma
["decay_CaDynamics_E2_soma", 20.0, 1000.0], #decay_soma
["gSK_E2bar_SK_E2_soma", 0, 0.1], #SK_soma
["gCa_HVAbar_Ca_HVA_apic[0]", 0, 0.0025], #CaHVA_apical
["gCa_LVAstbar_Ca_LVAst_apic[0]", 0, 0.1], #CaLVA_apical
["gamma_CaDynamics_E2_apic[0]", 0.0005, 0.05], #gamma_apical
["decay_CaDynamics_E2_apic[0]", 20.0, 200.0], #decay_apical
["gSK_E2bar_SK_E2_apic[0]", 0, 0.005], #SK_apical
["gCa_HVAbar_Ca_HVA_apic[1]", 0, 10*0.0025], #CaHVA_apical2 (Use 10-fold maximal value to take into account the possible hot region)
["gCa_LVAstbar_Ca_LVAst_apic[1]", 0, 10*0.1], #CaLVA_apical2 (Use 10-fold maximal value to take into account the possible hot region)
["gamma_CaDynamics_E2_apic[1]", 0.0005, 0.05], #gamma_apical2
["decay_CaDynamics_E2_apic[1]", 20.0, 200.0], #decay_apical2
["gSK_E2bar_SK_E2_apic[1]", 0, 0.005] ], #SK_apical2
[ ["gNaTa_tbar_NaTa_t_soma", 0, 4.0], #Nat_soma
["gNap_Et2bar_Nap_Et2_soma", 0, 0.01], #Nap_soma
["gK_Tstbar_K_Tst_soma", 0, 0.1], #Kt_soma
["gK_Pstbar_K_Pst_soma", 0, 1.0], #Kp_soma
["gSKv3_1bar_SKv3_1_soma", 0, 2.0], #Kv31_soma
["gImbar_Im_apic[0]", 0, 0.0005], #Im_apical
["gNaTa_tbar_NaTa_t_apic[0]", 0, 0.02], #Nat_apical
["gSKv3_1bar_SKv3_1_apic[0]", 0, 0.02], #Kv31_apical
["gImbar_Im_apic[1]", 0, 0.0005], #Im_apical2
["gNaTa_tbar_NaTa_t_apic[1]", 0, 0.02], #Nat_apical2
["gSKv3_1bar_SKv3_1_apic[1]", 0, 0.02] ] ] #Kv31_apical2
# Stimulus mediators (names and types of point processes):
# 0: Use the IClamp named "st1" at soma(0.5)
# 1: Use the EPSP-like stimulus named "syn1" at a segment on apical dendrite 620 um from the soma
# NAME TYPE WHERE AT A FIXED POS? AMPLITUDE NAME
# OR AT A DISTANCE
# FROM SOMA?
stimulus_types = [ ["st1", "IClamp", "soma", ["fixed", 0.5], "amp" ],
["syn1", "epsp", "apic", ["distance", 620], "imax" ] ]
# Types of data storage (what data to pass on to the objective functions):
# 0: The membrane potential (or Ca concentration) at t=13000 ms
# 1: The maximum of membrane potential on interval 10000..10200 ms
# 2: The membrane potential time series from 50 ms prior to beginning of stimulus to 200 ms post, use a 5-ms resolution
# 3: The membrane potential time series from 50 ms prior to beginning of stimulus to 200 ms post, use a 1-ms resolution
# 4: The membrane potential time series from 50 ms prior to beginning of stimulus to 200 ms post, use a 1-ms resolution, and the exact spike times
# 5: Just the number of spikes during interval 12000...15000 ms
# WHAT TYPE OF OUTPUT? VOLTAGE/[CA] AT A
# FIXED TIME (LAST TVEC BEFORE GIVEN TIME)
# OR MAXIMUM/NSPIKES DURING A GIVEN INTERVAL?
data_storage_types = [ ["fixed", 13000],
["max", [10000,10200] ],
["trace", [9950+5*x for x in range(0,51)] ],
["highrestrace", [9950+x for x in range(0,251)] ],
["highrestraceandspikes", [9950+x for x in range(0,251)] ],
["nspikes", [12000, 15000] ] ]
# Types of stimuli
# 0: Use IClamp ("st1") with onset at 10000 ms and 3000-ms duration
# 1: Use IClamp ("st1") with onset at 10000 ms and 100-ms duration
# 2: Use EPSP-like stimulus ("syn1") with onset at 10000 ms, rise time of 0.5 ms and decay time of 5.0 ms
# 3: Use IClamp ("st1") with onset at 10000 ms and 5000-ms duration
# 4: Use IClamp ("st1") with onset at 10000 ms and 5-ms duration
# STIMULUS MEDIATOR
# LIST OF POINT PROCESS PARAMETERS
stimuli = [ [ 0, [ ["del", 10000], ["dur", 3000] ] ],
[ 0, [ ["del", 10000], ["dur", 100] ] ],
[ 1, [ ["onset", 10000], ["tau0", 0.5], ["tau1", 5.0] ] ],
[ 0, [ ["del", 10000], ["dur", 5000] ] ],
[ 0, [ ["del", 10000], ["dur", 5] ] ] ]
# Types of recordings
# 0: Membrane potential at soma
# 1: Membrane potential at dendrites
# 2: Calcium concentration at apical dendrite
# NAME WHAT WHERE
# WHICH AT WHICH
# BRANCH LOCATIONS
recordings = [ [ "vsoma", "v", [ ["soma", [0.5] ] ] ],
[ "vdend", "v", [ ["apic", [0.05*x for x in range(0,21)] ],
["dend", [0.05*x for x in range(0,21)] ] ] ],
[ "cadend", "cai", [ ["apic", [0.05*x for x in range(0,21)] ] ] ] ]
# The setup for the objective functions. The structure consists of four steps, each with 2-4 objective functions, each of which contains
# the list of stimulus indices, the list of amplitudes, the recording index (or list of them if several), and the data storage index.
# STIMULUS INDEX
# AMPLITUDES
# RECORDING INDEX
# DATA STORAGE INDEX
snmf_setup = [ [ [ [0], [0.5], 1, 0 ], #STEP 1: MEMBRANE POTENTIAL STEADY STATE RESPONSE TO A NONZERO LONG DC,
[ [2], [0.5], 1, 1 ], # MEMBRANE POTENTIAL MAXIMUM AS A RESPONSE TO AN EPSP,
[ [1], [-1.0, 0, 1.0], 0, 2 ] ], # AND MEMBRANE POTENTIAL TRACE RESPONSE TO A SHORT (100 ms) DC
[ [ [0], [0, 0.5, 1.0], 1, 0 ], #STEP 2: MEMBRANE POTENTIAL STEADY STATE RESPONSE TO A ZERO AND NONZERO LONG DC
[ [1], [0.5, 1.0], 0, 2 ] ], # AND MEMBRANE POTENTIAL TRACE RESPONSE TO A SHORT (100 ms) DC
[ [ [1], [1.0, 2.0], 0, 3 ], #STEP 3: MEMBRANE POTENTIAL TRACE RESPONSE TO A SHORT (100 ms) STRONG DC,
[ [0], [0.5, 1.0], [1, 2], 0 ], # MEMBRANE POTENTIAL STEADY STATE RESPONSE TO A LONG DC,
# [CA] STEADY STATE RESPONSE TO A LONG DC,
[ [2], [1.0, 2.0], [1, 2], 1 ] ], # MEMBRANE POTENTIAL MAXIMUM AS A RESPONSE TO AN EPSP,
# AND [CA] MAXIMUM AS A RESPONSE TO AN EPSP
[ [ [1], [0.25, 0.5], 0, 4 ], #STEP 4: MEMBRANE POTENTIAL TRACE RESPONSE TO A 100 ms DC,
[ [4], [1.9], 0, 4 ], # MEMBRANE POTENTIAL TRACE RESPONSE TO A SHORT SOMATIC STIMULUS
[ [4,2], [[1.9, 0.5]], 0, 4 ], # MEMBRANE POTENTIAL TRACE RESPONSE TO COMBINATION OF SHORT SOMATIC AND APICAL STIMULI
[ [3], [0.78, 1.0, 1.9], 0, 5 ] ] ] # NUMBERS OF SPIKES AS A RESPONSE TO A LONG DC
# Data structure telling whether we can stop the evaluation of the objective function if there are no spikes when there should be none
stop_if_no_spikes = [ [ [0], [0], [0, 0, 0] ], # Propose that the simulation be stopped and high error values be given to the rest of the
[ [0, 0, 0], [0, 0] ], # objectives in case there are no spikes when there should be (1's denote such stimuli, 0's
[ [0, 0], [0, 0], [0, 0] ], # denote stimuli where this is not considered)
[ [0, 1], [0], [1], [1, 1, 1] ] ]
# Data structure telling whether we can stop the evaluation of the objective function if there are more spikes than announced
stop_if_more_spikes_or_as_many_as = [ [ [0], [0], [0, 0, 0] ], # Propose that the simulation be stopped and high error values be given to the rest of the
[ [0, 0, 0], [0, 0] ], # objectives in case there are more than x spikes when there should be much fewer (0's
[ [0, 0], [0, 0], [0, 0] ], # denote stimuli where this is not considered, x > 0 denotes stimuli where this is done
[ [2, 5], [5], [5], [100, 200, 300] ] ] # when nSpikes >= x)
# Return the full matrix of the step-wise fitting variables
def get_snmf_variables():
global snmf_variables
return snmf_variables
# Return the parameter names that should be fixed at the istep:th step
def get_fixed_params(istep):
global snmf_variables
fixed_params = []
for istep2 in range(0,istep):
for ivar in range(0,len(snmf_variables[istep2])):
fixed_params.append(snmf_variables[istep2][ivar][0])
return fixed_params
# Return the parameter names that should be fitted during the istep:th step
def get_variable_params(istep):
global snmf_variables
variable_params = []
if istep < len(snmf_variables):
for ivar in range(0,len(snmf_variables[istep])):
variable_params.append(snmf_variables[istep][ivar][0])
return variable_params
# Return the stimulus array
def get_stimuli():
global stimuli
return stimuli
# Return the stimulus types array
def get_stimulus_types():
global stimulus_types
return stimulus_types
# Return the data storage array
def get_data_storage_types():
global data_storage_types
return data_storage_types
# Return the recordings array
def get_recordings():
global recordings
return recordings
# Return the stimulus-recording setup array
def get_setup():
global snmf_setup
return snmf_setup
def get_nspike_restrictions():
global stop_if_no_spikes, stop_if_more_spikes_or_as_many_as
return [stop_if_no_spikes, stop_if_more_spikes_or_as_many_as]
