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neuron_cls.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Aug 27 13:38:21 2017
@author: daniel
"""
from neuron import h
import parameters as p
import synapse as s
import spine as sp
import sys
import re
import random as rnd
import json
filename = p.params_for_learning_functions
class Neuron(object):
"""Generic neuron class."""
def __init__(self):
self.create_morphology()
self.insert_channels()
self.esyn = []
self.isyn = []
self.spines = []
def create_morphology(self):
"""Create the cell morphology, including axial resistance and membrane capacitance."""
raise NotImplementedError('"create_morphology() is not implemented."')
def insert_channels(self):
"""Insert channels in the cell."""
raise NotImplementedError("insert_channels() is not implemented.")
def create_sectionlists(self):
"""Build subset lists. This defines 'all', but subclasses may
want to define others. If overridden, call super() to include 'all'."""
self.all = h.SectionList()
self.all.wholetree(sec=self.soma)
def connect2target(self, target, thresh=10):
"""Make a new NetCon with this cell's membrane
potential at the soma as the source (i.e. the spike detector)
onto the target passed in (i.e. a synapse on a cell).
Subclasses may override with other spike detectors."""
nc = h.NetCon(self.soma(1)._ref_v, target, sec = self.soma)
nc.threshold = thresh
return nc
def insert_synapse(self, syntype, sec, pos, add_spine = 0, on_spine = 0):
if add_spine and on_spine:
print("Arguments add_spine and on_spine can't simultaneously be 1")
sys.exit(-1)
if add_spine:
s_ind = [int(si) for si in re.findall("\d+", sec.name())]
s_ind = s_ind[0]
self.num_spines_on_dends[s_ind] += 1
spine_name = 'spine_' + sec.name() + '(' + str(pos) + ')'
self.spines.append(sp.Spine(sec, spine_name))
self.spines[-1].attach(sec, pos, 0)
self.spines[-1].syn_on = 1
sec = self.spines[-1].head
s.spinepos = pos
pos = 0.5
if on_spine:
empty_spines = [spine for spine in self.spines if (spine.parent == sec and spine.syn_on == 0)]
if empty_spines == []:
print("There are no empty spines on dendrite %s" % sec.name())
sys.exit(-1)
else:
sec = empty_spines[0].head
s.spinepos = pos
pos = 0.5
empty_spines[0].syn_on = 1
syn = s.Synapse()
syn.type = syntype
syn.sec = sec
syn.pos = pos
if syntype in ['expsyn', 'expsyn_plateau']:
syn.obj = h.ExpSyn(sec(pos))
syn.obj.tau = p.esyn_tau
syn.obj.e = p.e_esyn
self.esyn.append(syn)
return syn
elif syntype == 'inhexpsyn' or syntype == 'inhexpsyn_plateau':
syn.obj = h.InhExpSyn(sec(pos))
if syntype == 'inhexpsyn':
syn.obj.tau = p.isyn_tau
elif syntype == 'inhexpsyn_plateau':
syn.obj.tau = p.isyn_plateau_tau
syn.obj.e = p.e_gaba
self.isyn.append(syn)
return self.isyn[-1]
elif syntype == 'exp2syn':
syn.obj = h.Exp2Syn(sec(pos))
syn.obj.e = p.e_esyn
syn.obj.tau2 = p.tau2_exp2syn
syn.obj.tau1 = p.tau1_exp2syn
self.esyn.append(syn)
return syn
elif syntype == 'inhexp2syn':
syn.obj = h.InhExp2Syn(sec(pos))
syn.obj.e = p.e_gaba
syn.obj.tau2 = p.tau2_inhexp2syn
syn.obj.tau1 = p.tau1_inhexp2syn
self.isyn.append(syn)
return syn
elif syntype == 'tmGlut':
syn.obj = h.tmGlut(sec(pos))
syn.obj.tau1_nmda = p.tau1_NMDA
syn.obj.tau2_nmda = p.tau2_NMDA
syn.obj.nmda_ratio = p.ratio_glutamate_syn
syn.obj.U = 0.9
syn.obj.tauF = 5.0
# syn.obj.tau = 50
# syn.obj.tauR = 100.0
self.esyn.append(syn)
return syn
elif syntype == 'glutamate' or syntype == 'glutamate_plateau':
syn.obj = h.glutamate(sec(pos))
syn.obj.mg = p.Mg
syn.obj.eta = p.eta
syn.obj.alpha = p.alpha
syn.obj.tau1_nmda = p.tau1_NMDA
syn.obj.tau2_nmda = p.tau2_NMDA
syn.obj.ratio = p.ratio_glutamate_syn
self.esyn.append(syn)
return syn
elif syntype in ['glutamate_ica_nmda', 'glutamate_xor_test'] :
syn.obj = h.glutamate_ica_nmda(sec(pos))
syn.obj.mg = p.Mg
syn.obj.eta = p.eta
syn.obj.alpha = p.alpha
syn.obj.tau1_nmda = p.tau1_NMDA
syn.obj.tau2_nmda = p.tau2_NMDA
syn.obj.w_ampa = p.gAMPAmax_plateau
syn.obj.w_nmda = p.gNMDAmax_plateau
syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
self.esyn.append(syn)
return syn
elif syntype in ['AMPA' ,'AMPA_test', 'AMPA_pf', 'AMPA_stp']:
if syntype in ['AMPA', 'AMPA_pf']:
syn.obj = h.AMPA(sec(pos))
elif syntype in ['AMPA_stp']:
syn.obj = h.AMPA_stp(sec(pos))
syn.obj.U = p.U
syn.obj.u0 = p.u0
elif syntype == 'AMPA_test':
syn.obj = h.AMPA_test(sec(pos))
syn.obj.weight = p.weight
syn.obj.gmax = p.gmaxAMPA_spillover
if syntype == 'AMPA_pf':
syn.obj.gmax = p.gmaxAMPA_pf
self.esyn.append(syn)
return syn
elif syntype in [ 'NMDA', 'NMDA_test', 'NMDAe', 'NMDA_pf', 'NMDA_stp']:
if syntype in ['NMDA', 'NMDA_pf']:
syn.obj = h.NMDA(sec(pos))
elif syntype in ['NMDA_stp']:
syn.obj = h.NMDA_stp(sec(pos))
syn.obj.U = p.U
syn.obj.u0 = p.u0
elif syntype == 'NMDA_test':
syn.obj = h.NMDA_test(sec(pos))
elif syntype == 'NMDAe':
syn.obj = h.NMDAe(sec(pos))
syn.obj.mg = p.Mg
syn.obj.eta = p.eta
syn.obj.alpha = p.alpha
syn.obj.Cdur = p.Cdur
if syntype in ['NMDA', 'NMDA_test', 'NMDA_stp']:
syn.obj.gmax = p.gmaxNMDA_spillover
elif syntype in ['NMDA_pf']:
syn.obj.gmax = p.gmaxNMDA_pf
elif syntype in ['NMDAe']:
syn.obj.gmax = p.gmaxNMDAe_spillover
syn.obj.Cdur_init = p.eCdur_init
syn.obj.Cdur_factor = p.eCdur_factor
syn.obj.weight = p.exglu_weight
syn.obj.Beta = p.Beta
syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
if syntype == 'NMDA_test':
syn.obj.weight = p.weight
self.esyn.append(syn)
return syn
elif syntype in ['adaptive_shom_AMPA', 'adaptive_shom_AMPA_stp']:
if syntype in ['adaptive_shom_AMPA']:
syn.obj = h.adaptive_shom_AMPA(sec(pos))
else:
syn.obj = h.adaptive_shom_AMPA_stp(sec(pos))
syn.obj.U = p.U
syn.obj.u0 = p.u0
syn.obj.gmax = p.gmaxAMPA_spillover
self.esyn.append(syn)
return syn
elif syntype == 'adaptive_pf_AMPA':
syn.obj = h.adaptive_pf_AMPA(sec(pos))
syn.obj.gmax = p.gmaxAMPA_pf
self.esyn.append(syn)
return syn
elif syntype in ['adaptive_shom_NMDA','adaptive_shom_NMDA_stp','adaptive_my_shom_NMDA']:
if syntype in ['adaptive_shom_NMDA']:
syn.obj = h.adaptive_shom_NMDA(sec(pos))
elif syntype in ['adaptive_shom_NMDA_stp']:
syn.obj = h.adaptive_shom_NMDA_stp(sec(pos))
syn.obj.U = p.U
syn.obj.u0 = p.u0
else:
syn.obj = h.adaptive_my_shom_NMDA(sec(pos))
syn.obj.mg = p.Mg
syn.obj.eta = p.eta
syn.obj.alpha = p.alpha
syn.obj.gmax = p.gmaxNMDA_spillover
syn.obj.Beta = p.Beta
syn.obj.Cdur = p.Cdur
syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
syn.obj.w0 = p.weight
syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP
syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD
syn.obj.learning_rate_thresh_KD_LTD = p.learning_rate_thresh_KD_LTD
syn.obj.KD1 = p.KD1
syn.obj.n1 = p.n1
syn.obj.KD2 = p.KD2
syn.obj.n2 = p.n2
syn.obj.KD_LTD = p.KD_LTD
syn.obj.n_LTD = p.n_LTD
self.esyn.append(syn)
return syn
elif syntype == 'adaptive_pf_NMDA':
syn.obj = h.adaptive_pf_NMDA(sec(pos))
syn.obj.mg = p.Mg
syn.obj.eta = p.eta
syn.obj.alpha = p.alpha
syn.obj.gmax = p.gmaxNMDA_pf
syn.obj.Beta = p.Beta
syn.obj.Cdur = p.Cdur_pf
syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
syn.obj.w0 = p.weight
syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD_pf
syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP
syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD
syn.obj.KD_LTD = p.KD_LTD_pf
syn.obj.n_LTD = p.n_LTD_pf
self.esyn.append(syn)
return syn
elif syntype == 'adaptive_NMDAe':
syn.obj = h.adaptive_NMDAe(sec(pos))
syn.obj.mg = p.Mg
syn.obj.eta = p.eta
syn.obj.alpha = p.alpha
syn.obj.Erev = p.erev_NMDA
syn.obj.gmax = p.gmaxNMDAe_spillover
syn.obj.Beta = p.Beta
syn.obj.Cdur = p.eCdur
syn.obj.Cdur_init = p.eCdur_init
syn.obj.Cdur_factor = p.eCdur_factor
syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
self.esyn.append(syn)
return syn
# elif syntype == 'adaptive_glutamate':
# syn.obj = h.adaptive_glutamate(sec(pos))
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.w0 = p.gAMPAmax_plateau
# syn.obj.wmax = p.gAMPAmax_plateau*p.LTP_factor
# syn.obj.wmin = p.gAMPAmax_plateau*p.LTD_factor
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
# syn.obj.thresh_LTD = p.thresh_LTD
# syn.obj.thresh_LTP = p.thresh_LTP
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
# syn.obj.NMDA_AMPA_ratio == p.ratio_distributed_synapses
#
# syn.obj.tau1_nmda = p.tau1_NMDA
# syn.obj.tau2_nmda = p.tau2_NMDA
#
# # if add_spine:
## sec = self.spines[-1].parent
## pos = self.spines[-1].pos
## h.setpointer(sec(pos)._ref_cali, 'cali', syn.obj)
#
# self.esyn.append(syn)
# return syn
# elif syntype == 'adaptive_sglutamate':
# syn.obj = h.adaptive_sglutamate(sec(pos))
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.w0 = p.gAMPAmax_plateau
# syn.obj.NMDA_AMPA_ratio = p.ratio_distributed_synapses
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
#
# syn.obj.tau1_nmda = p.tau1_NMDA
# syn.obj.tau2_nmda = p.tau2_NMDA
#
# with open(filename, 'r') as f:
# to_read = json.load(f)
# res_dict = json.loads(to_read)
# params_LTP = res_dict['cai_nmda_params_by_dend']
# params_LTD = res_dict['cali_params_by_dend']
# r = re.findall("\[\d+\]", sec.name())
# r = [int(num) for elem in r for num in re.findall("\d+", elem)]
# KD1, n1, KD2, n2, factor_LTP = params_LTP[0]
# KD_LTD, n_LTD, factor_LTD = params_LTD[0]
#
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP*0.25
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD*100
# syn.obj.n1 = p.n1
# syn.obj.KD2 = p.KD2
# syn.obj.n2 = p.n2
# syn.obj.KD_LTD = p.KD_LTD
# syn.obj.n_LTD = p.n_LTD
#
# self.esyn.append(syn)
# return syn
# elif syntype in ['adaptive_glutamate_hom']:
# if p.random_weights == True:
# w_ampa = rnd.uniform(p.gAMPAmax_plateau*p.LTD_factor, p.gAMPAmax_plateau*p.LTP_factor)
# else:
# w_ampa = p.gAMPAmax_plateau
# syn.obj = h.adaptive_glutamate_hom(sec(pos))
#
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
# syn.obj.w0 = w_ampa
# syn.obj.wmax = p.gAMPAmax_plateau*p.LTP_factor_di
# syn.obj.wmin = p.gAMPAmax_plateau*p.LTD_factor_di
# syn.obj.NMDA_AMPA_ratio = p.ratio_distributed_synapses
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
# syn.obj.thresh_LTP_0 = p.thresh_LTP
# syn.obj.thresh_LTD_0 = p.thresh_LTD
# syn.obj.thresh_LTP_min = p.thresh_LTP_min
# syn.obj.thresh_LTD_min = p.thresh_LTD_min
#
# syn.obj.thresh_LTP_max = p.thresh_LTP_max
# syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP_di
# syn.obj.thresh_LTD_max = p.thresh_LTD_max
# syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD_di
#
# syn.obj.tau1_nmda = p.tau1_NMDA
# syn.obj.tau2_nmda = p.tau2_NMDA
#
# self.esyn.append(syn)
# return syn
#
# elif syntype in ['adaptive_glutamate_shom']:
# if p.random_weights == True:
# weight = rnd.uniform(p.gAMPAmax_plateau*p.LTD_factor, p.gAMPAmax_plateau*p.LTP_factor)
# else:
# weight = p.gAMPAmax_plateau
# syn.obj = h.adaptive_glutamate_shom(sec(pos))
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
# syn.obj.w0 = weight
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
# syn.obj.thresh_LTP_0 = p.thresh_LTP
# syn.obj.thresh_LTD_0 = p.thresh_LTD
# syn.obj.hthresh_LTP_0 = p.hthresh_LTP
# syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP
# syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD
# syn.obj.NMDA_AMPA_ratio = p.ratio_distributed_synapses
# syn.obj.tau1_nmda = p.tau1_NMDA
# syn.obj.tau2_nmda = p.tau2_NMDA
## if add_spine:
## sec = self.spines[-1].parent
## pos = self.spines[-1].pos
## h.setpointer(sec(pos)._ref_cali, 'cali', syn.obj)
#
# self.esyn.append(syn)
# return syn
#
# elif syntype in ['adaptive_glutamate_cshom']:
# if p.random_weights == True:
# weight = rnd.uniform(p.gAMPAmax_plateau*p.LTD_factor, p.gAMPAmax_plateau*p.LTP_factor)
# else:
# weight = p.gAMPAmax_plateau
# syn.obj = h.adaptive_glutamate_cshom(sec(pos))
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
# syn.obj.w0 = weight
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
# syn.obj.thresh_LTP_0 = p.thresh_LTP
# syn.obj.thresh_LTD_0 = p.thresh_LTD
# syn.obj.hthresh_LTP_0 = p.hthresh_LTP
# syn.obj.lthresh_LTP_min = p.lthresh_LTP_min
# syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP*2.5
# syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD*2.5
# syn.obj.NMDA_AMPA_ratio = p.ratio_distributed_synapses
# syn.obj.tau1_nmda = p.tau1_NMDA
# syn.obj.tau2_nmda = p.tau2_NMDA
# syn.obj.steepness_LTP = p.steepness_LTP
# syn.obj.steepness_LTD = p.steepness_LTD
# if add_spine:
# sec = self.spines[-1].parent
# pos = self.spines[-1].pos
# h.setpointer(sec(pos)._ref_cali, 'cali', syn.obj)
# self.esyn.append(syn)
# return syn
# elif syntype == 'adaptive_AMPA':
# syn.obj = h.adaptive_AMPA(sec(pos))
# syn.obj.gmax = p.gmaxAMPA_spillover
#
# syn.obj.w0 = p.weight
# syn.obj.wmax = p.weight*p.LTP_factor
# syn.obj.wmin = p.weight*p.LTD_factor
# syn.obj.learning_rate_w_LTP = p.learning_rate_w
# syn.obj.learning_rate_w_LTD = p.learning_rate_w
# syn.obj.thresh_LTD = p.thresh_LTD
# syn.obj.thresh_LTP = p.thresh_LTP
#
# self.esyn.append(syn)
# return syn
# elif syntype == 'adaptive_sAMPA':
# syn.obj = h.adaptive_sAMPA(sec(pos))
# syn.obj.gmax = p.gmaxAMPA_spillover
#
# self.esyn.append(syn)
# return syn
#
# elif syntype == 'adaptive_NMDA':
# syn.obj = h.adaptive_NMDA(sec(pos))
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.Erev = p.erev_NMDA
# syn.obj.gmax = p.gmaxNMDA_spillover
# syn.obj.Beta = p.Beta
# syn.obj.Cdur = p.eCdur
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
#
# syn.obj.w0 = p.weight
# syn.obj.learning_rate_w_LTP = p.learning_rate_w
# syn.obj.learning_rate_w_LTD = p.learning_rate_w
# syn.obj.thresh_LTD = p.thresh_LTD
# syn.obj.thresh_LTP = p.thresh_LTP
#
# syn.obj.Cdur_init = p.eCdur_init
# syn.obj.Cdur_factor = p.eCdur_factor
#
# self.esyn.append(syn)
# return syn
# elif syntype == 'adaptive_sNMDA':
# syn.obj = h.adaptive_sNMDA(sec(pos))
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.Erev = p.erev_NMDA
# syn.obj.gmax = p.gmaxNMDA_spillover
# syn.obj.Beta = p.Beta
# syn.obj.Cdur = p.Cdur
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
#
# syn.obj.w0 = p.weight
# with open(filename, 'r') as f:
# to_read = json.load(f)
# res_dict = json.loads(to_read)
# params_LTP = res_dict['cai_nmda_params_by_dend']
# params_LTD = res_dict['cali_params_by_dend']
# r = re.findall("\[\d+\]", sec.name())
# r = [int(num) for elem in r for num in re.findall("\d+", elem)]
# print(r[0])
# KD1, n1, KD2, n2, factor_LTP = params_LTP[p.independent_dends.index(r[0])]
# KD_LTD, n_LTD, factor_LTD = params_LTD[p.independent_dends.index(r[0])]
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
# syn.obj.KD1 = p.KD1
# syn.obj.n1 = p.n1
# syn.obj.KD2 = p.KD2
# syn.obj.n2 = p.n2
# syn.obj.KD_LTD = p.KD_LTD
# syn.obj.n_LTD = p.n_LTD
#
# self.esyn.append(syn)
# return syn
# elif syntype == 'adaptive_hom_AMPA':
# syn.obj = h.adaptive_hom_AMPA(sec(pos))
# syn.obj.gmax = p.gmaxAMPA_spillover
#
# syn.obj.w0 = p.weight
# syn.obj.wmax = p.weight*p.LTP_factor
# syn.obj.wmin = p.weight*p.LTD_factor
# syn.obj.learning_rate_w_LTP = p.learning_rate_w
# syn.obj.learning_rate_w_LTD = p.learning_rate_w
# syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP
# syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD
# syn.obj.thresh_LTD_0 = p.thresh_LTD
# syn.obj.thresh_LTP_0 = p.thresh_LTP
# syn.obj.thresh_LTD_min = p.thresh_LTD_min
# syn.obj.thresh_LTP_min = p.thresh_LTP_min
# syn.obj.thresh_LTD_max = p.thresh_LTD_max
# syn.obj.thresh_LTP_max = p.thresh_LTP_max
# syn.obj.LTD_thresh_factor = p.LTD_thresh_factor
#
# self.esyn.append(syn)
# return syn
# elif syntype == 'adaptive_zahra_AMPA':
# syn.obj = h.adaptive_zahra_AMPA(sec(pos))
# syn.obj.gmax = p.gmaxAMPA_spillover
#
# self.esyn.append(syn)
# return syn
#
# elif syntype == 'adaptive_cshom_AMPA':
# syn.obj = h.adaptive_cshom_AMPA(sec(pos))
# syn.obj.gmax = p.gmaxAMPA_spillover
#
# syn.obj.width = p.width
# syn.obj.w0 = p.weight
# syn.obj.wmax = p.weight*p.LTP_factor
# syn.obj.wmin = p.weight*p.LTD_factor
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
# syn.obj.thresh_LTD_0 = p.thresh_LTD
# syn.obj.thresh_LTP_0 = p.thresh_LTP
# syn.obj.hthresh_LTP_0 = p.hthresh_LTP
# syn.obj.hthresh_LTP_const = p.hthresh_LTP_const
# syn.obj.n = p.Hill_coefficient
# syn.obj.LTD_thresh_factor = p.LTD_thresh_factor
# syn.obj.lthresh_LTP_min = p.lthresh_LTP_min
#
# syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP
# syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD
# syn.obj.steepness_LTP = p.steepness_LTP
# syn.obj.steepness_LTD = p.steepness_LTD
# self.esyn.append(syn)
# return syn
# elif syntype in ['adaptive_hom_NMDA']:
# syn.obj = h.adaptive_hom_NMDA(sec(pos))
#
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.gmax = p.gmaxNMDA_spillover
# syn.obj.Beta = p.Beta
# syn.obj.Cdur = p.eCdur
# syn.obj.Cdur_init = p.eCdur_init
# syn.obj.Cdur_factor = p.eCdur_factor
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
#
# syn.obj.w0 = p.weight
# syn.obj.wmax = p.weight*p.LTP_factor
# syn.obj.wmin = p.weight*p.LTD_factor
# syn.obj.learning_rate_w_LTP = p.learning_rate_w
# syn.obj.learning_rate_w_LTD = p.learning_rate_w
# syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP
# syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD
# syn.obj.thresh_LTD_0 = p.thresh_LTD
# syn.obj.thresh_LTP_0 = p.thresh_LTP
# syn.obj.thresh_LTD_min = p.thresh_LTD_min
# syn.obj.thresh_LTP_min = p.thresh_LTP_min
#
# syn.obj.thresh_LTD_max = p.thresh_LTD_max
# syn.obj.thresh_LTP_max = p.thresh_LTP_max
# syn.obj.LTD_thresh_factor = p.LTD_thresh_factor
#
# self.esyn.append(syn)
# return syn
# elif syntype == 'adaptive_zahra_NMDA':
# syn.obj = h.adaptive_zahra_NMDA(sec(pos))
#
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.gmax = p.gmaxNMDA_spillover
# syn.obj.Beta = p.Beta
# syn.obj.Cdur = p.Cdur
#
# syn.obj.w0 = p.weight
# syn.obj.rate_ltp = 0.00004
# syn.obj.rate_ltd = 0.0007
# syn.obj.rate_ltp_tresh = 0.000006
# syn.obj.rate_ltd_thrsh = 0.00005
# syn.obj.tremin = 0.03
# self.esyn.append(syn)
# return syn
#
# elif syntype == 'adaptive_cshom_NMDA':
# syn.obj = h.adaptive_cshom_NMDA(sec(pos))
#
# syn.obj.mg = p.Mg
# syn.obj.eta = p.eta
# syn.obj.alpha = p.alpha
# syn.obj.gmax = p.gmaxNMDA_spillover
# syn.obj.Beta = p.Beta
# syn.obj.Cdur = p.Cdur
# syn.obj.n = p.Hill_coefficient
# syn.obj.nmda_ca_fraction = p.nmda_ca_fraction
#
# syn.obj.width = p.width
# syn.obj.w0 = p.weight
# syn.obj.learning_rate_w_LTP = p.learning_rate_w_LTP
# syn.obj.learning_rate_w_LTD = p.learning_rate_w_LTD
# syn.obj.thresh_LTD_0 = p.thresh_LTD
# syn.obj.thresh_LTP_0 = p.thresh_LTP
# syn.obj.hthresh_LTP_0 = p.hthresh_LTP
# syn.obj.hthresh_LTP_const = p.hthresh_LTP_const
# syn.obj.LTD_thresh_factor = p.LTD_thresh_factor
# syn.obj.lthresh_LTP_min = p.lthresh_LTP_min
#
# syn.obj.learning_rate_thresh_LTP = p.learning_rate_thresh_LTP
# syn.obj.learning_rate_thresh_LTD = p.learning_rate_thresh_LTD
# syn.obj.steepness_LTP = p.steepness_LTP
# syn.obj.steepness_LTD = p.steepness_LTD
# self.esyn.append(syn)
# return syn
else:
print("From method cell.insert_synapse")
print("Syntype '%s' not supported" % syntype)
sys.exit(-1)
def max_dist(self, axon_excluding=True):
if not hasattr(self, 'somalist'):
raise NotImplementedError("create_sectionlists() is not implemented or attribute somalist not defined")
h.distance(sec=self.somalist[0])
dmax = 0
for sec in self.all:
if axon_excluding and sec.name().find('axon') == 0:
continue
dmax = max(dmax, h.distance(1, sec=sec))
return dmax
def get_nsegs(self):
"""Returns the number of segments in the neuron model."""
nsegs = 0
for sec in self.all:
nsegs += sec.nseg
return nsegs
def set_nsegs(self):
"""Sets the number of segments in each section of the neuron model
according to n = 2*int(L/40) + 1, where L is the length of the section."""
for sec in self.all:
sec.nseg = 2*int(sec.L/40.0)+1
if hasattr(self, 'axonlist'):
for sec in self.axonlist:
sec.nseg = 2 # two segments in axon initial segment
def total_dend_length(self):
"""Returns the total dendritic length."""
total_length = 0
for dend in self.dendlist:
total_length += dend.L
return total_length
def increase_dend_res(self, dend_list, mult):
for d in dend_list:
self.dendlist[d].nseg *= mult