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knox.py
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knox.py
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''' USAGE example
import knox as kx
h.run()
import graph as g
g.TCraster(kx.thalDict)
'''
from neuron import h
import os, sys, json
import numpy as np
import pylab as plt
import pickle as pkl
from collections import OrderedDict as OD
datestr = os.popen('datestring').read()
for hoc in ['stdrun.hoc', 'TC.tem', 'RE.tem', 'sPY.tem', 'sIN.tem']: h.load_file(hoc)
ncorticalcells, nthalamiccells = 100, 100
axondelay, narrowdiam, widediam = 0, 5, 10
it2l = ['it2WT', 'it2C456S', 'it2R788C', 'it2', 'itrecustom'] # it2 is RE, it is TC channel
# RERE RETCa RETCb TCRE PYPY PYIN INPYa INPYb PYRE PYTC TCPY TCIN
synparams = OD({'synaptic weights J neurophys': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.1500, 0.03, 1.2, 0.01, 1.2, 0.4),
'75% IN->PY weight (0.1125)': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.1125, 0.03, 1.2, 0.01, 1.2, 0.4),
'50% IN->PY weight (0.075)': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.0750, 0.03, 1.2, 0.01, 1.2, 0.4),
'40% IN->PY weight (0.06)': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.0600, 0.03, 1.2, 0.01, 1.2, 0.4),
'25% IN->PY weight (0.0375)': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.0375, 0.03, 1.2, 0.01, 1.2, 0.4),
'10% IN->PY weight (0.015)': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.0150, 0.03, 1.2, 0.01, 1.2, 0.4),
'0% IN->PY A weight': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.0000, 0.03, 1.2, 0.01, 1.2, 0.4),
'orig IN->PY A weight': (0.20, 0.02, 0.04, 0.2, 0.6, 0.2, 0.1500, 0.03, 1.2, 0.01, 1.2, 0.4),
'10% IN->PY A weight better RERE for new synapses': (0.12, 0.02, 0.04, 0.2, 0.6, 0.2, 0.0000, 0.03, 1.2, 0.01, 1.2, 0.4),
'0% RERE and RETC': (0.00, 0.00, 0.04, 0.2, 0.6, 0.2, 0.1500, 0.03, 1.2, 0.01, 1.2, 0.4)})
# gababapercent, gababpercent were both == 1
def barname (mech='it'):
'''return the name of a gbar (max conductance) for a given mechanism name'''
l = []
pname, ms = h.ref(''), h.MechanismStandard(mech, 1)
for i in range(int(ms.count())):
ms.name(pname, i)
l.append(pname[0])
ll=[x for x in l if 'bar' in x]
if len(ll)!=1: raise Exception("Can't identify proper parameter for %s: %s"%(mech, ll))
return ll[0]
def mkcells ():
types = ['TC', 'RE', 'PY', 'IN']
tD = {k: {'cel': [], 'ncl': [], 'stims': [], 'predi': {}} for k in types}
for pre, ei in zip(types, ['ampapost','gabaapost','ampapost','gabaapost']):
tD[pre]['targ'] = {}
for post in types:
tD[pre]['targ'][post] = ei
tD['PY']['targ']['PY'], tD['TC']['targ']['PY'] = 'ampapostPY', 'ampapostTC'
for k in ['TC','RE']:
tD[k]['num'] = nthalamiccells
for i in range(nthalamiccells):
tD[k]['cel'].append(h.__getattribute__('s'+k)())
for k in ['PY','IN']:
tD[k]['num'] = ncorticalcells
for i in range(ncorticalcells):
tD[k]['cel'].append(h.__getattribute__('s'+k)())
tD['RE']['T'], tD['TC']['T']={n:barname(n) for n in it2l}, {n:barname(n) for n in ['ittccustom', 'it']}
for v in tD.itervalues(): v['gnabar'] = v['cel'][0].soma[0].gnabar_hh2
return tD
def mksyns (tD):
global dbl
dbl=[]
for k in tD.keys(): tD[k]['lambda'] = {k1:narrowdiam for k1 in tD.keys()} # default narrowdiam
for zero in ['INTC', 'INRE', 'TCTC', 'ININ', 'REPY', 'REIN']: tD[zero[:2]]['lambda'][zero[2:]] = 0
for wide in ['PYRE', 'PYTC', 'TCPY', 'TCIN']: tD[wide[:2]]['lambda'][wide[2:]] = widediam
for k in tD.keys():
for k1 in tD.keys():
if tD[k]['lambda'][k1] > 0:
connect(k,k1,tD)
for tyl in tD.values():
for i,ce in enumerate(tyl['cel']):
ncl = h.cvode.netconlist(ce,'','')
if len(ncl)>0: tyl['ncl'].append(ncl[0]) # just take one
else: print 'No netcons found for cell %s'%str(ce)
def assignSyns (k='orig IN->PY A weight', tD=None):
'''Assign weight strengthes for synapses'''
if not tD: tD=thalDict
w = synparams[[x for x in synparams.keys() if k in x][0]] # allow abbreviating these long titles
print w
syid = ['RERE', 'RETCga', 'RETCgb', 'TCRE', 'PYPY', 'PYIN', 'INPYga', 'INPYgb', 'PYRE', 'PYTC', 'TCPY', 'TCIN']
ty = None
for sy, wt in zip(syid, w):
prty, poty = sy[:2], sy[2:4]
if len(sy)==6: syty=sy[-2:] # ga or gb (can also use later for am AMPA vs nm NMDA)
for nc in tD[poty]['predi'][prty]:
nc.weight[0]=wt/(2*tD[poty]['lambda'][prty] + 1) # denominator will be to big at the edges since no wraparound
def connect (kpr, kpo, tD):
global dbl
lam = tD[kpr]['lambda'][kpo]
for npost,cepost in enumerate(tD[kpo]['cel']):
if not kpr in tD[kpo]['predi']: tD[kpo]['predi'][kpr] = [] # or can use get()
for pre in range(npost-lam, npost+lam+1):
if pre >= 0 and pre < tD[kpo]['num']: # no wraparound
if kpr=='RE' and pre==50: dbl.append(cepost)
tD[kpo]['predi'][kpr].append(h.NetCon(tD[kpr]['cel'][pre].soma[0](0.5)._ref_v,
cepost.__getattribute__(tD[kpr]['targ'][kpo]),
0, axondelay, 1, sec=tD[kpr]['cel'][pre].soma[0]))
def insertchans ():
h.tstop=1e3
for vals in thalDict.values():
for ce in vals['cel']:
ce.soma[0].insert('hh2nafjr')
ce.soma[0].gnabar_hh2nafjr = 0.0
for ce in thalDict['RE']['cel']:
sec=ce.soma[0]
for mech in it2l:
ce.soma[0].insert(mech)
h('%s.gcabar_%s = 0.0'%(str(sec),mech))
def setchans (mun=3, pnafjr=0.0, gnamult=1.0, gcabar=None, gcavfac=1.0, tyli=['TC', 'RE', 'PY', 'IN']):
it2= it2l[mun]
ms = h.MechanismStandard(it2, 1)
print "Using %s channels"%it2
gcab = gcabar if gcabar else 3e-3
for k in ['TC', 'RE']:
vals=thalDict[k]
for ce in vals['cel']:
ce.soma[0].gnabar_hh2nafjr = pnafjr * gnamult * vals['gnabar'] # what is this??
ce.soma[0].gnabar_hh2 = (1-pnafjr) * gnamult * vals['gnabar']
for ce in thalDict['RE']['cel']: # just set the RE one for now; corrD=3.777 for surface correction (Cav32RE3cc.hoc:105:257)
sec=ce.soma[0]
for v in thalDict['RE']['T'].values(): sec.__setattr__(v, 0.0) # turn all off
sec.__setattr__(thalDict['RE']['T'][it2], gcab*gcavfac)
def zeroselfs ():
selfconns = [nc for nc in h.List('NetCon') if nc.precell()==nc.postcell()]
for nc in selfconns:
for x in range(int(nc.wcnt())):
nc.weight[x]=0.0
def setstims (ctype='PY', nl=[11,30,49,68], amp=0.7, dly=10.0, dur=50.0):
stims = thalDict[ctype]['stims']
if len(nl)>len(stims): stims += [h.IClamp() for i in range(len(nl) - len(stims))] # extend stim list
for x in stims:
if x.get_segment(): x.amp=0.0 # clear
for x,n in zip(stims,nl): # nl may be shorter than stim
loc=thalDict[ctype]['cel'][n].soma[0](0.5)
x.loc(loc)
x.amp, x.dur, x.delay = amp, dur, dly
# recording
def recv (thresh=-5):
for k,v in thalDict.iteritems():
v['spkt'], v['spkid'], v['vrec'] = h.Vector(5e3), h.Vector(5e3), []
for j, (ce, nc) in enumerate(zip(v['cel'],v['ncl'])):
nc.record(v['spkt'], v['spkid'], j)
for j in [30, 70]:
ce = v['cel'][j]
ve = h.Vector(h.tstop/h.dt+10)
v['vrec'].append(ve)
ve.record(ce.soma[0](0.5)._ref_v)
def allsetup ():
global thalDict
thalDict = mkcells() # creates dict used for cell lists and all other lists
mksyns(thalDict)
insertchans() # special Na chan, Ca chans
recv() # record vol
setchans() # type and density of channels
assignSyns() # sets weights
# zeroselfs() # remove self connections
setstims()
allsetup()
'''
COMMENT: testing sequence when opened from interpreter
import knox as kx
reload(kx)
td=tD=thalDict = kx.mkcells()
kx.thalDict=td # set up the global
kx.mksyns(td)
kx.setup()
kx.recv()
kx.setchans() # used to be setparams()
kx.assignSyns()
kx.zeroselfs() # remove self connections
kx.setstims()
import graph as g
g.mkfig() # 1 time only
g.TCraster(kx.thalDict)
'''