from neuron import h, gui
import nrnutils_ss as nu
import toolbox as tb
from Spine import Spine
import pyr_2005 as pyr
from random import sample,choice
import numpy as np
# import ipdb
class Spiny_branch():
def __init__(self,
p,
rank=0):
h.celsius = p['temperature']
# Load the branch 93 with its channels
self.cell = pyr.cell(p['Vrest'],
p['RmDend'],
p['RaAll'],
p['CmDend'],
records=[
{'section':'soma',
'variable':'v',
'location':0.5,
'unit':'mV'},
{'section':'branch_base',
'variable':'v',
'location':0.5,
'unit':'mV'}],
use_channels=True
)
# # Set branhc erev
# for sec in self.cell.branch_sl:
# sec.ena = p['erev_na']
# sec.ek = p['erev_k']
# if p['check']:
# print sec.ena,sec.ek
# ipdb.set_trace()
# self.cell.balance_currents(p['Vrest'], check = p['check'])
# done in the hoc file
# self.cell.record(['vm','spikes'])
# Spine mechanisms
self.cad = nu.Mechanism('cad', depth = 0.05, tauca = 12, cainf = 100e-6) # depth in um, tau in ms, cainf in mM
self.pas = nu.Mechanism('pas', e=p['Vrest'], g=1./p['Rm'])
self.cal = nu.Mechanism ('cal', gcalbar=p['gcalbar'])
self.can = nu.Mechanism ('can', gcanbar=p['gcanbar'])
self.cat = nu.Mechanism ('cat', gcatbar=p['gcatbar'])
# self.kad = nu.Mechanism ('kad', gkabar=0)
# self.na3 = nu.Mechanism ('na3', gbar=0)
# self.kdr = nu.Mechanism ('kdr', gkdrbar=0)
h.use_mcell_ran4()
self.MCell_Ran4_lowindex = 42
h.mcell_ran4_init(self.MCell_Ran4_lowindex)
self.noiseRandObj = h.Random() #Provides NOISE with random stream
self.MCell_Ran4_highindex = [self.noiseRandObj.MCellRan4(12345)]
self.noiseRandObj.uniform(0,1)
self.branch_segments = [[sec,seg] for sec in list(self.cell.branch38.values()) for seg in sec]
self.branch_segments_2 = [[sec,seg] for sec in list(self.cell.branch8.values()) for seg in sec]
self.branch_segments_3 = [[sec,seg] for sec in list(self.cell.branch37.values()) for seg in sec]
# self.spine_segments = sample(self.branch_segments,p['nspines'])
# self.spine_segments = self.branch_segments[0:len(self.branch_segments):3]
# # Some spines potentiate before others, thus we set those spines to be at the dendritic tip where
# # the BPAP has larger amplitud yielding a larger Ca2+ transient, more fused vesicles, more pro/mBDNF in the syn cleft
# # with a resulting earlier/faster potentiation.
# self.spine_segments = [self.branch_segments[-1],
# self.branch_segments[-1],
# self.branch_segments[-1],
# self.branch_segments[-1]]# 4 spines at the branch tip
# self.spine_segments.extend(self.branch_segments[-7:-4]) # 3 spines in the middle
# self.spine_segments.extend(self.branch_segments[-15:-12]) # 4 near the branching point
# self.spine_segments.extend([self.branch_segments[-20]])
# self.spine_segments.extend([self.branch_segments[-22]])
# # print len(self.branch_segments), self.branch_segments[-22:-19]
# self.spine_segments.extend(self.branch_segments_2[-22:-19])
# self.spine_segments.extend(self.branch_segments_2[-22:-19])
# # self.spine_segments.extend([self.branch_segments[-25]])
# # self.spine_segments.extend([self.branch_segments[-26]])
# # self.spine_segments.extend([self.branch_segments_3[-1]])
# # self.spine_segments.extend([self.branch_segments_3[-3]])
# print "LENGTH",len(self.spine_segments)
self.seg_indexes = [-1,-1,-1,-1,-7,-6,-5,-15,-14,-13,-20,-22] # 12 spines [0:11]
self.seg_indexes_2 =[-22,-21,-20,-22,-21,-20] # 6 extra spines [12:17]
# # Generate randomly selected set
# # self.seg_indexes = sample(range(0,len(self.branch_segments)-15),p['nspines'])
# self.seg_indexes = np.random.choice(range(len(self. branch_segments)),size=p['nspines'])
# self.seg_indexes_2 = np.random.choice(range(len(self.branch_segments_2)),size=p['nspines_2'])
# Seg_indexes are saved in the sim data file.
self.spine_segments = [self.branch_segments[idx] for idx in self.seg_indexes]
self.spine_segments.extend([self.branch_segments_2[idx] for idx in self.seg_indexes_2])
print([str(sn) for sn in self.spine_segments])
# Spines
self.spines = []
for i,s in enumerate(self.spine_segments):
self.spines.append(Spine('Spine_%g'%i,p,
neck_mechanisms=[self.pas],
connection_point = 0,
parent = s,
head_mechanisms=[self.cad,
self.pas,
self.cal, self.can,
self.cat],
noiseRandObj = self.noiseRandObj,
balance_currents=True,
highindex=self.MCell_Ran4_highindex[-1]))
self.MCell_Ran4_highindex.append(self.noiseRandObj.MCellRan4())
# Set specific RM11 and RMD time constants
self.spines[0].head.RMECB.tau_RMLTP11 = self.spines[0].head.RMECB.tau_RMLTP11/3
self.spines[1].head.RMECB.tau_RMLTP11 = self.spines[0].head.RMECB.tau_RMLTP11
self.spines[2].head.RMECB.tau_RMLTP11 = self.spines[0].head.RMECB.tau_RMLTP11
self.spines[3].head.RMECB.tau_RMLTP11 = self.spines[3].head.RMECB.tau_RMLTP11
self.spines[4].head.RMECB.tau_RMLTP11 = self.spines[3].head.RMECB.tau_RMLTP11
self.spines[5].head.RMECB.tau_RMLTP11 = self.spines[3].head.RMECB.tau_RMLTP11 * 1.5
self.spines[6].head.RMECB.tau_RMLTP11 = self.spines[3].head.RMECB.tau_RMLTP11 * 1.5
self.spines[7].head.RMECB.tau_RMLTP11 = self.spines[7].head.RMECB.tau_RMLTP11 * 1.7
self.spines[8].head.RMECB.tau_RMLTP11 = self.spines[7].head.RMECB.tau_RMLTP11
self.spines[9].head.RMECB.tau_RMLTP11 = self.spines[7].head.RMECB.tau_RMLTP11
self.spines[10].head.RMECB.tau_RMLTP11 = self.spines[3].head.RMECB.tau_RMLTP11 * 2
self.spines[11].head.RMECB.tau_RMLTP11 = self.spines[3].head.RMECB.tau_RMLTP11
# # Set specific BDNF constants
# for s_i,s in enumerate(self.spines[12:18]):
# s.head.BDNF.theta_cai_BDNF = 0.045 # set a low cai threshold these spines on branch8
# s.head.internal_nc.threshold = s.head.BDNF.theta_cai_BDNF
# s.head.RMECB.theta_cai_RMBLK = 0.052 # set a low cai threshold these spines on branch8
# s.head.RMECB.theta_cai_RM = 0.006 # set a low cai threshold these spines on branch8
# s.head.BDNF.theta_gAMPA = 0.02 # set a normal threshold for test
# s.head.BDNF.alpha_gAMPA = 1.5 # set a high LTP
# s.head.BDNF.v_BDNF = 0.002*1.0 # Increase BDNF release at these spines
# self.spines[12].head.BDNF.theta_gAMPA = 0.06
# self.spines[13].head.BDNF.theta_gAMPA = 0.05
# self.spines[14].head.BDNF.theta_gAMPA = 0.075
# self.spines[15].head.BDNF.theta_gAMPA = 0.075
# self.spines[16].head.BDNF.theta_gAMPA = 0.08
# self.spines[17].head.BDNF.theta_gAMPA = 0.11
for s_i,s in enumerate(self.spines):
print(("spine ", s_i, s.head.BDNF.theta_gAMPA))
def plot_branch(self,
variable,
type='mech',
label='',
location=0.5,
tmin=0,
tmax=5,
xmin=-80,
xmax=40,
view=None,
show=1,
color='k',
line=1,
graph=None):
# Convert color to number
colors = {'r':2,'k':1,'g':4,'b':3,'o':5,'mr':7,'m':9,'y':8}
color = colors[color]
import neuron.gui
if graph is None:
self.graph = h.Graph(show)
graph = self.graph
h.graphList[0].append(graph)
graph.size(tmin, tmax, xmin, xmax)
if view is not None:
graph.view(view[0],view[1],view[2],
view[3],view[4],view[5],view[6],view[7])
if not label:
label = variable
if 'mech' in type:
graph.addvar('%s(%g)'%(label,location),
'%s(%g)' % (variable, location),
color, line, sec=self.cell.branch_base)
if 'pp' in type:
graph.addvar('%s(%g)'%(label,location),
getattr(getattr(self,variable[0]),
'_ref_'+variable[1]),
color, line, sec=self.cell.branch_base)
return graph
def plot_soma(self,
variable,
type='mech',
label='',
location=0.5,
tmin=0,
tmax=5,
xmin=-80,
xmax=40,
view=None,
show=1,
color='k',
line=1,
graph=None,
position=None):
# Convert color to number
colors = {'r':2,'k':1,'g':4,'b':3,'o':5,'mr':7,'m':9,'y':8,
'1':1,'2':2,'3':3,'4':4,'5':5,'6':6,'7':7,'8':8,'9':9}
color = colors[str(color)]
if position is None:
position = [0.8,0.9]
import neuron.gui
if graph is None:
self.graph = h.Graph(show)
graph = self.graph
h.graphList[0].append(graph)
graph.size(tmin, tmax, xmin, xmax)
if view is not None:
graph.view(view[0],view[1],view[2],
view[3],view[4],view[5],view[6],view[7])
if not label:
label = variable
if 'mech' in type:
graph.addvar('%s(%g)'%(label,location),
'%s(%g)' % (variable, location),
color, line, sec=self.cell.soma)
if 'pp' in type:
graph.addvar('%s(%g)'%(label,location),
getattr(getattr(self,variable[0]),
'_ref_'+variable[1]),
color, line, sec=self.cell.branch_base)
graph.flush()
return graph
