-
Notifications
You must be signed in to change notification settings - Fork 1
/
libcell.py
545 lines (469 loc) · 19.5 KB
/
libcell.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
# ----------------------------------------------------------
# Library of cell classes and functions
#
# Naoki Hiratani, N.Hiratani@gmail.com
#
# Based on the model by Dr. Tiago Branco
# ----------------------------------------------------------
import numpy as np
import neuron
from neuron import h
from neuron import load_mechanisms
from neuron import gui
from math import *
import random as pyrnd
import matplotlib.pyplot as plt
import scipy.special as scisp
h('objref nil')
# ----------------------------------------------------------
# MODELS
class L23(object):
def __init__(self):
h('xopen("./L23.hoc")')
props(self)
self._geom()
self._topol()
self._biophys()
self._dist_from_soma()
def _geom(self):
self.axon = h.Section()
self.axon.L = 300
self.axon.diam = 1
def _topol(self):
self.soma = h.soma
self.axon.connect(self.soma,1,0)
#self.axon = h.axon
self.dends = [] #list of dendritic section
for sec in h.allsec():
self.dends.append(sec)
self.dends.remove(self.soma) # Remove soma from the list
self.dends.remove(self.axon) # and the Axon
dnames = ['dend1_1','dend1_11','dend1_111','dend1_1111','dend1_1112','dend1_112','dend1_1121','dend1_1122','dend1_12','dend1_121','dend1_1211','dend1_1212','dend1_122','dend1_1221','dend1_1222','dend1_12221','dend1_12222','dend2_1','dend2_11','dend2_111','dend2_1111','dend2_1112','dend2_112','dend2_1121','dend2_1122','dend2_12','dend2_121','dend2_1211','dend2_12111','dend2_121111','dend2_121112','dend2_12112','dend2_121121','dend2_1211211','dend2_1211212','dend2_12112121','dend2_12112122','dend2_121122','dend2_1212','dend2_12121','dend2_121211','dend2_121212','dend2_12122','dend2_122','dend2_1221','dend2_12211','dend2_12212','dend2_1222','dend2_12221','dend2_12222','dend3_1','dend3_11','dend3_111','dend3_1111','dend3_1112','dend3_11121','dend3_11122','dend3_112','dend3_1121','dend3_1122','dend3_12','dend3_121','dend3_1211','dend3_1212','dend3_12121','dend3_12122','dend3_121221','dend3_121222','dend3_1212221','dend3_1212222','dend3_12122221','dend3_12122222','dend3_121222221','dend3_1212222211','dend3_12122222111','dend3_12122222112','dend3_1212222212','dend3_121222222','dend3_1212222221','dend3_1212222222','dend3_12122222221','dend3_12122222222','dend3_122','dend3_1221','dend3_12211','dend3_12212','dend3_1222','dend4_1','dend4_11','dend4_111','dend4_1111','dend4_1112','dend4_11121','dend4_11122','dend4_112','dend4_1121','dend4_1122','dend4_12','dend4_121','dend4_122','dend4_1221','dend4_1222','dend4_12221','dend4_12222']
def _biophys(self):
for sec in h.allsec():
sec.cm = self.CM
sec.insert('pas')
sec.e_pas = self.E_PAS
sec.g_pas = 1.0/self.RM
sec.Ra = self.RA
sec.nseg = int(ceil(sec.L/5.0)) #5um segmentation
def _dist_from_soma(self):
self.dists = [] #distance from the soma to dendrites
h('soma distance(0.0,0.5)')
lidx = 0
for sec in h.allsec():
if lidx < len(self.dends):
h('''proc calc_distance(){
disttmp = distance(1.0,0.0)}''')
h.calc_distance()
self.dists.append(h.disttmp)
lidx += 1
self.branchdists = [] #dendritic distance between two branches
l1idx = 0
for sec1 in h.allsec():
if l1idx < len(self.dends):
self.branchdists.append([])
h('distance()')
l2idx = 0
for sec2 in h.allsec():
if l2idx < len(self.dends):
h('''proc calc_distance(){
disttmp = distance(1.0,0.0)}''')
h.calc_distance()
self.branchdists[l1idx].append( h.disttmp )
l2idx += 1
l1idx += 1
# ----------------------------------------------------------
# INSTRUMENTATION FUNCTIONS
def props(model):
# Passive properties
model.CM = 1.0
model.RM = 7000.0
model.RA = 100
model.E_PAS = -75
model.CELSIUS = 35
# Active properties
model.Ek = -90
model.Ena = 60
model.Eca = 140
model.gna_axon = 1000
model.gkv_axon = 100
model.gna_soma = 1000
model.gkv_soma = 100
model.gkm_soma = 2.2
model.gkca_soma = 3
model.gca_soma = 0.5
model.git_soma = 0.0003
model.gna_dend = 27#80
model.gna_dend_hotSpot = 600
model.gkv_dend = 1#3
model.gkm_dend = 0.3#1
model.gkca_dend = 3
model.gca_dend = 0.5
model.git_dend = 0.00015
model.gh_dend = 0
def init_active(model, axon=False, soma=False, dend=True, dendNa=False,
dendCa=False):
if axon:
model.axon.insert('na'); model.axon.gbar_na = model.gna_axon
model.axon.insert('kv'); model.axon.gbar_kv = model.gkv_axon
model.axon.ena = model.Ena
model.axon.ek = model.Ek
if soma:
model.soma.insert('na'); model.soma.gbar_na = model.gna_soma
model.soma.insert('kv'); model.soma.gbar_kv = model.gkv_soma
model.soma.insert('km'); model.soma.gbar_km = model.gkm_soma
model.soma.insert('kca'); model.soma.gbar_kca = model.gkca_soma
model.soma.insert('ca'); model.soma.gbar_ca = model.gca_soma
model.soma.insert('it'); model.soma.gbar_it = model.git_soma
#model.soma.insert('cad');
model.soma.ena = model.Ena
model.soma.ek = model.Ek
model.soma.eca = model.Eca
if dend:
for d in model.dends:
d.insert('na'); d.gbar_na = model.gna_dend*dendNa
d.insert('kv'); d.gbar_kv = model.gkv_dend
d.insert('km'); d.gbar_km = model.gkm_dend
d.insert('kca'); d.gbar_kca = model.gkca_dend
d.insert('ca'); d.gbar_ca = model.gca_dend*dendCa
d.insert('it'); d.gbar_it = model.git_dend*dendCa
#d.insert('cad')
d.ena = model.Ena
d.ek = model.Ek
d.eca = model.Eca
def init_params(model, Kin, gmax, gI, uk_min, sd_bias, release_prob):
#simulation control
model.trials = 1001# #trial number
#synapses
model.Min = 200 #number of input neurons
model.Kin = Kin #redanduncy in synaptic connections
model.Nin = model.Kin*model.Min #number of synaptic inputs
model.gmax = gmax#0.0015 #standard conductance [muS]
model.sd_bias = sd_bias #bias in synaptic distribution
model.uk_min = uk_min #lower boundary condition of uks
model.uk_max = 1.0 #upper boundary condition of uks
#rewiring parameters
model.ukthreshold = model.uk_min #spine cutoff threshold
model.ukinit = 1.0/float(Kin) #model.uk_min #size of a newly created spine
model.taumr = 10.0 #the time constant for mean firing rate
model.rewiring_freq = 0.2 #relative frequency of rewiring
model.pre_rates_th = 0.05 #threshold for the presynaptic firing rate in supervised trials
#input structure
model.spn_rate = 1.5*pi #spontaneous firing rate
model.epsilon = 0.01 #the minimum relative rate
model.min_rate = model.epsilon*model.spn_rate
model.release_prob = release_prob# presynaptic release probability
model.p_theta = 0.0 #preferred orientation
model.n_theta = model.p_theta + pi/2.0 #non-preferred orientation
model.rfdist_zero = 1.0 # standard distance between somatic and synaptic receptive fields
model.rfdist_min = 0.01 # minimum distance between somatic and synaptic receptive fields
model.rfdist_max = 3.0 # maximum distance between somatic and synaptic receptive fields
model.kappa_zero = 2.0 # orientation selectivity
model.kappa_phi = 4.0 # association field selectivity
model.krfdist_const = exp(model.kappa_phi)/100.0 #constant for rfdist in calculation of k_i (to avoid numerical instability)
#inhibitory synapses
model.inhN = 200 #Number of inhibitory synapses
model.gI = gI #inhibitory conductance
model.Inh_thr = -90.0 #mV
#stimulation protocol
model.sstart = 20.0 #starting timing of the stimulation (from the initiation of the simulation)
model.sduration = 20.0 #stimulation duration[ms]
model.snoise = 0.0 #noise in spike train
def plot_morphology(model,locs):
h('access soma')
h('objref sh')
h('sh = new PlotShape(0)')
h('sh.size(-300,300,-299.522,299.522)')
h('''proc plot_synapse(){
xdtmp = x3d($1)
ydtmp = y3d($1)
ctmp = 7
sh.mark(xdtmp,ydtmp,"o",6,ctmp,4)
}''')
lidx = 0
for sec in h.allsec():
if lidx < len(model.dends):
for i in range(len(locs)):
loc = locs[i]
if loc[0] == lidx:
h('n3dtmp = n3d()')
ndtmp = int(floor(h.n3dtmp*loc[1]))
h.plot_synapse(ndtmp,model.preidx[i])
lidx += 1
h('sh.view(-300, -400.522, 600, 900.043, 265, 450, 200.64, 400.32)')
def add_Estims(model,locs,sstart=20.0,sinterval=20,snumber=1,snoise=0):
model.Estimlist = []
lidx = 0
for loc in locs:
Estim = h.NetStim()
Estim.interval = sinterval
Estim.number = snumber
Estim.start = sstart
Estim.noise = snoise
model.Estimlist.append(Estim)
lidx += 1
def add_Istims(model,inh_locs,sstart=20.0,sinterval=20,snumber=1,snoise=0):
model.Istimlist = []
lidx = 0
for loc in inh_locs:
Istim = h.NetStim()
Istim.interval = sinterval
Istim.number = snumber
Istim.start = sstart
Istim.noise = snoise
model.Istimlist.append(Istim)
lidx += 1
#select dendritic locations for calculation of the unit EPSPs
def calc_locs_kappa(model):
dL = 5.0
locs = []
lidx = 0
nidx = 0
for sec in model.dends:
nsec = int(ceil(sec.L/dL))
for sidx in range( nsec ):
locs.append([])
locs[nidx].append( lidx )
locs[nidx].append( (sidx+0.5)/float(nsec) )
if locs[nidx][1] < 0.0 or locs[nidx][1] > 1.0:
print lidx,nidx,locs[nidx][0],locs[nidx][1]
nidx += 1
lidx += 1
#print '#kappa_locs',nidx
return locs
#Uniform selection of dendritic locations of the N synaptic contacts
def calc_locs_uniform(model):
locs = []
Ltot = 0.0
for sec in model.dends:
Ltot += sec.L
dL = Ltot/(model.Nin+1.0)
Ltmp = 0.0
nidx = 0
lidx = 0
for sec in model.dends:
Ltmp += sec.L
while nidx*dL < Ltmp and Ltmp <= (nidx+1)*Ltmp:
if nidx > 0 and nidx <= model.Nin:
locs.append([])
locs[nidx-1].append(lidx)
locs[nidx-1].append( 1.0 - (Ltmp-nidx*dL)/(sec.L) )
nidx += 1
lidx += 1
#print Ltot, dL, len(locs)
return locs
#Random selection of dendritic locations of the N synaptic contacts from a Beta distribution characterized by (bsd, 2-bsd)
def calc_locs_random(model):
locs = []
Lcums = []
Lcums.append(0.0)
for sec in model.dends:
Lcums.append(Lcums[-1] + sec.L)
Ltot = Lcums[-1]
dmax = 500.0#max(model.dists)
bsda = model.sd_bias; bsdb = 2.0 - model.sd_bias #parameters for Beta distribution
Zbeta = 10.0*scisp.beta(bsda,bsdb)
#print dmax, bsda, bsdb, Zbeta
nidx = 0
while(nidx < model.Nin):
Ltmp = Ltot*pyrnd.random()
for j in range(len(model.dends)):
if Lcums[j] <= Ltmp and Ltmp < Lcums[j+1]:
rdtmp = (model.dists[j] + (Ltmp-Lcums[j]))/dmax
if pyrnd.random() < pow(rdtmp,bsda-1.0)*pow(1.0-rdtmp,bsdb-1.0)/Zbeta:
locs.append([])
locs[nidx].append(j)
locs[nidx].append( (Ltmp-Lcums[j])/(Lcums[j+1]-Lcums[j]) )
nidx += 1
#print Ltot, len(locs)
return locs
#Uniform selection of dendritic locations of the inhN synaptic contacts
def calc_inh_locs_uniform(model):
inh_locs = []
Ltot = 0.0
for sec in model.dends:
Ltot += sec.L
dL = Ltot/(model.inhN+1.0)
Ltmp = 0.0
nidx = 0
lidx = 0
for sec in model.dends:
Ltmp += sec.L
while nidx*dL < Ltmp and Ltmp <= (nidx+1)*Ltmp:
if nidx > 0 and nidx <= model.inhN:
inh_locs.append([])
inh_locs[nidx-1].append(lidx)
inh_locs[nidx-1].append( 1.0 - (Ltmp-nidx*dL)/(sec.L) )
nidx += 1
lidx += 1
print Ltot, dL, len(inh_locs)
return inh_locs
#restricted resampling: the posisiton of the new synapse is restricted to the set of dendritic branches where original connections were made
def restricted_resampling(model, locs, i):
loctmp = []
Lcums = []
Lcums.append(0.0)
for j in range(model.Nin):
if model.preidx[j] == model.preidx[i]:
Lcums.append(Lcums[-1] + model.dends[locs[j][0]].L)
Ltot = Lcums[-1]
Ltmp = Ltot*pyrnd.random()
lidx = 0
for j in range(model.Nin):
if model.preidx[j] == model.preidx[i]:
if Lcums[lidx] <= Ltmp and Ltmp < Lcums[lidx+1]:
loctmp.append( locs[j][0])
loctmp.append( (Ltmp-Lcums[lidx])/(Lcums[lidx+1]-Lcums[lidx]) )
lidx += 1
return loctmp
def calc_dist_bt_syns(model,locs):#calculate the distance between synapses
disttmps = []
for i1 in range(model.Nin):
jidx = model.preidx[i1]
d1 = locs[i1][0]
for i2 in range(i1+1,model.Nin):
if model.preidx[i2] == jidx:
d2 = locs[i2][0]
disttmp = model.branchdists[d1][d2] + model.dends[d1].L*locs[i1][1] + model.dends[d2].L*locs[i2][1]
disttmps.append( disttmp )
return disttmps
#presynaptic index allocation
def allocate_syns(model):
rndidx = range(model.Nin)
pyrnd.shuffle(rndidx)
model.preidx = []
for i in range(model.Nin):
model.preidx.append(0)
for iidx in range(model.Nin):
model.preidx[rndidx[iidx]] = iidx/model.Kin
#add AMPA synapses
def add_AMPAsyns(model, locs=[[0, 0.5]], gmax=0.5, tau1=0.5, tau2=2.5):
model.AMPAlist = []
model.ncAMPAlist = []
for lidx in range(len(locs)):
loc = locs[lidx]
AMPA = h.Exp2Syn(float(loc[1]), sec=model.dends[int(loc[0])])
AMPA.tau1 = tau1
AMPA.tau2 = tau2
#NC = h.NetCon(h.nil, AMPA, 0, 0, gmax)
NC = h.NetCon(model.Estimlist[lidx], AMPA, 0, 0, gmax)
model.AMPAlist.append(AMPA)
model.ncAMPAlist.append(NC)
def add_GABAsyns(model, inh_locs=[[0, 0.5]], gI=0.5, tau1=0.5, tau2=2.5):
model.GABAlist = []
model.ncGABAlist = []
for lidx in range(len(inh_locs)):
inh_loc = inh_locs[lidx]
GABA = h.Exp2Syn(float(inh_loc[1]), sec=model.dends[int(inh_loc[0])])
GABA.tau1 = tau1
GABA.tau2 = tau2
GABA.e = model.Inh_thr
NC = h.NetCon(model.Istimlist[lidx], GABA, 0, 0, model.gI)
model.GABAlist.append(GABA)
model.ncGABAlist.append(NC)
def rewire_synapse(model,loc,lidx,tau1=0.5,tau2=2.5):
AMPAtmp = h.Exp2Syn(float(loc[1]), sec=model.dends[int(loc[0])])
AMPAtmp.tau1 = tau1
AMPAtmp.tau2 = tau2
NCtmp = h.NetCon(model.Estimlist[lidx], AMPAtmp, 0, 0, model.gmax)
model.AMPAlist[lidx] = AMPAtmp
model.ncAMPAlist[lidx] = NCtmp
def generate_pre_tuning(model):
model.phis = [] #polar direction of RF
model.thetas = [] #preferred orientation
model.rfdists = [] #Receptive field distance
rfdist_zero = model.rfdist_zero
rfdist_min = model.rfdist_min
rfdist_max = model.rfdist_max
for j in range(model.Min):
model.phis.append( 2.0*pi*np.random.random() )
model.thetas.append( pi*np.random.random() )
model.rfdists.append( rfdist_min + (rfdist_max-rfdist_min)*np.random.random() )
def generate_log_rates(model, thetao):
rfdist_zero = model.rfdist_zero
krfdist_const = model.krfdist_const
kappa_zero = model.kappa_zero
kappa_phi = model.kappa_phi
min_rate = model.min_rate
spn_rate = model.spn_rate
rates = []; log_rates = []
for j in range(model.Min):
phi = model.phis[j]; rfdist = model.rfdists[j]; theta = model.thetas[j]
kappa_r = rfdist_zero*exp(kappa_phi*cos(2.0*(phi-thetao)))/(rfdist + krfdist_const)
ktmp = sqrt( kappa_zero*kappa_zero + kappa_r*kappa_r + 2*kappa_zero*kappa_r*cos( 2.0*(theta-thetao) ) )
rates.append( spn_rate*exp(-rfdist/rfdist_zero)*scisp.i0(ktmp)/(2*pi*scisp.i0(kappa_zero)*scisp.i0(kappa_r)) )
if rates[-1] > min_rate:
log_rates.append( log(rates[-1]/min_rate) )
else:
log_rates.append( 0.0 )
return rates, log_rates
def generate_input_rates(model):
# spontaneous firing rates
model.s_rates = []
for j in range(model.Min):
model.s_rates.append( model.spn_rate )
# firing rate for the preferred orientation
p_rates, log_p_rates = generate_log_rates(model, model.p_theta)
model.p_rates = p_rates
model.log_p_rates = log_p_rates
# firing rate for the non-preferred orientation
n_rates, log_n_rates = generate_log_rates(model, model.n_theta)
model.n_rates = n_rates
model.log_n_rates = log_n_rates
#print "p_rate, n_rate: ", np.average(p_rates), np.average(n_rates)
def generate_prespikes(model, pre_rates):
release_prob = model.release_prob
Min = model.Min; Nin = model.Nin
true_pre_spikes = []; pre_spikes = []
for j in range(Min):
#Poisson pre-spikes
true_pre_spikes.append( np.random.poisson( pre_rates[j] ) )
for i in range(Nin):
jidx = model.preidx[i]
pre_spikes.append( np.random.binomial( true_pre_spikes[jidx], release_prob ) )
return true_pre_spikes, pre_spikes
def generate_inputs(model, uks, pre_spikes):
release_prob = model.release_prob
for i in range(model.Nin):
if pre_spikes[i] > 0:
model.Estimlist[i].interval = model.sduration/float(pre_spikes[i])
else:
model.Estimlist[i].interval = model.sduration
model.Estimlist[i].number = pre_spikes[i]
model.Estimlist[i].start = model.sstart + (model.Estimlist[i].interval)*np.random.random()
model.Estimlist[i].noise = model.snoise
weight_tmp = model.gmax*uks[i]/release_prob
#weight_tmp = model.gmax*uks[i]
NC = h.NetCon(model.Estimlist[i], model.AMPAlist[i], 0, 0, weight_tmp)
model.ncAMPAlist[i] = NC
def generate_inh_inputs(model, true_pre_spikes):
inhN = model.inhN
Nin = model.Nin; Min = model.Min
Erate = 0.0
for i in range(Nin):
Erate += true_pre_spikes[ model.preidx[i] ]/float(Nin)
Irate = ( float(Nin)/float(inhN) )*Erate
Ispikes = np.random.poisson( Irate, (inhN) )
#print Ispikes
for i in range(inhN):
if Ispikes[i] > 0:
model.Istimlist[i].interval = model.sduration/float(Ispikes[i])
else:
model.Istimlist[i].interval = model.sduration
model.Istimlist[i].number = Ispikes[i]
model.Istimlist[i].start = model.sstart + (model.Istimlist[i].interval)*np.random.random()
model.Istimlist[i].noise = model.snoise
NC = h.NetCon(model.Istimlist[i], model.GABAlist[i], 0, 0, model.gI)
model.ncGABAlist[i] = NC
# ----------------------------------------------------------
# SIMULATION RUN
def simulate(model):
trec, vrec = h.Vector(), h.Vector()
trec.record(h._ref_t)
vrec.record(model.soma(0.5)._ref_v)
h.celsius = model.CELSIUS
#h.FInitializeHandler(1, initSpikes2)
h.finitialize(model.E_PAS)
neuron.run(model.tstop)
return np.array(trec), np.array(vrec)