In [1]:
def set_axes(axis):
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.spines['bottom'].set_position(('outward', 10))
axis.spines['left'].set_position(('outward', 10))
axis.yaxis.set_ticks_position('left')
axis.xaxis.set_ticks_position('bottom')
In [2]:
def get_viridis():
_viridis_data = [[0.267004, 0.004874, 0.329415],
[0.268510, 0.009605, 0.335427],
[0.269944, 0.014625, 0.341379],
[0.271305, 0.019942, 0.347269],
[0.272594, 0.025563, 0.353093],
[0.273809, 0.031497, 0.358853],
[0.274952, 0.037752, 0.364543],
[0.276022, 0.044167, 0.370164],
[0.277018, 0.050344, 0.375715],
[0.277941, 0.056324, 0.381191],
[0.278791, 0.062145, 0.386592],
[0.279566, 0.067836, 0.391917],
[0.280267, 0.073417, 0.397163],
[0.280894, 0.078907, 0.402329],
[0.281446, 0.084320, 0.407414],
[0.281924, 0.089666, 0.412415],
[0.282327, 0.094955, 0.417331],
[0.282656, 0.100196, 0.422160],
[0.282910, 0.105393, 0.426902],
[0.283091, 0.110553, 0.431554],
[0.283197, 0.115680, 0.436115],
[0.283229, 0.120777, 0.440584],
[0.283187, 0.125848, 0.444960],
[0.283072, 0.130895, 0.449241],
[0.282884, 0.135920, 0.453427],
[0.282623, 0.140926, 0.457517],
[0.282290, 0.145912, 0.461510],
[0.281887, 0.150881, 0.465405],
[0.281412, 0.155834, 0.469201],
[0.280868, 0.160771, 0.472899],
[0.280255, 0.165693, 0.476498],
[0.279574, 0.170599, 0.479997],
[0.278826, 0.175490, 0.483397],
[0.278012, 0.180367, 0.486697],
[0.277134, 0.185228, 0.489898],
[0.276194, 0.190074, 0.493001],
[0.275191, 0.194905, 0.496005],
[0.274128, 0.199721, 0.498911],
[0.273006, 0.204520, 0.501721],
[0.271828, 0.209303, 0.504434],
[0.270595, 0.214069, 0.507052],
[0.269308, 0.218818, 0.509577],
[0.267968, 0.223549, 0.512008],
[0.266580, 0.228262, 0.514349],
[0.265145, 0.232956, 0.516599],
[0.263663, 0.237631, 0.518762],
[0.262138, 0.242286, 0.520837],
[0.260571, 0.246922, 0.522828],
[0.258965, 0.251537, 0.524736],
[0.257322, 0.256130, 0.526563],
[0.255645, 0.260703, 0.528312],
[0.253935, 0.265254, 0.529983],
[0.252194, 0.269783, 0.531579],
[0.250425, 0.274290, 0.533103],
[0.248629, 0.278775, 0.534556],
[0.246811, 0.283237, 0.535941],
[0.244972, 0.287675, 0.537260],
[0.243113, 0.292092, 0.538516],
[0.241237, 0.296485, 0.539709],
[0.239346, 0.300855, 0.540844],
[0.237441, 0.305202, 0.541921],
[0.235526, 0.309527, 0.542944],
[0.233603, 0.313828, 0.543914],
[0.231674, 0.318106, 0.544834],
[0.229739, 0.322361, 0.545706],
[0.227802, 0.326594, 0.546532],
[0.225863, 0.330805, 0.547314],
[0.223925, 0.334994, 0.548053],
[0.221989, 0.339161, 0.548752],
[0.220057, 0.343307, 0.549413],
[0.218130, 0.347432, 0.550038],
[0.216210, 0.351535, 0.550627],
[0.214298, 0.355619, 0.551184],
[0.212395, 0.359683, 0.551710],
[0.210503, 0.363727, 0.552206],
[0.208623, 0.367752, 0.552675],
[0.206756, 0.371758, 0.553117],
[0.204903, 0.375746, 0.553533],
[0.203063, 0.379716, 0.553925],
[0.201239, 0.383670, 0.554294],
[0.199430, 0.387607, 0.554642],
[0.197636, 0.391528, 0.554969],
[0.195860, 0.395433, 0.555276],
[0.194100, 0.399323, 0.555565],
[0.192357, 0.403199, 0.555836],
[0.190631, 0.407061, 0.556089],
[0.188923, 0.410910, 0.556326],
[0.187231, 0.414746, 0.556547],
[0.185556, 0.418570, 0.556753],
[0.183898, 0.422383, 0.556944],
[0.182256, 0.426184, 0.557120],
[0.180629, 0.429975, 0.557282],
[0.179019, 0.433756, 0.557430],
[0.177423, 0.437527, 0.557565],
[0.175841, 0.441290, 0.557685],
[0.174274, 0.445044, 0.557792],
[0.172719, 0.448791, 0.557885],
[0.171176, 0.452530, 0.557965],
[0.169646, 0.456262, 0.558030],
[0.168126, 0.459988, 0.558082],
[0.166617, 0.463708, 0.558119],
[0.165117, 0.467423, 0.558141],
[0.163625, 0.471133, 0.558148],
[0.162142, 0.474838, 0.558140],
[0.160665, 0.478540, 0.558115],
[0.159194, 0.482237, 0.558073],
[0.157729, 0.485932, 0.558013],
[0.156270, 0.489624, 0.557936],
[0.154815, 0.493313, 0.557840],
[0.153364, 0.497000, 0.557724],
[0.151918, 0.500685, 0.557587],
[0.150476, 0.504369, 0.557430],
[0.149039, 0.508051, 0.557250],
[0.147607, 0.511733, 0.557049],
[0.146180, 0.515413, 0.556823],
[0.144759, 0.519093, 0.556572],
[0.143343, 0.522773, 0.556295],
[0.141935, 0.526453, 0.555991],
[0.140536, 0.530132, 0.555659],
[0.139147, 0.533812, 0.555298],
[0.137770, 0.537492, 0.554906],
[0.136408, 0.541173, 0.554483],
[0.135066, 0.544853, 0.554029],
[0.133743, 0.548535, 0.553541],
[0.132444, 0.552216, 0.553018],
[0.131172, 0.555899, 0.552459],
[0.129933, 0.559582, 0.551864],
[0.128729, 0.563265, 0.551229],
[0.127568, 0.566949, 0.550556],
[0.126453, 0.570633, 0.549841],
[0.125394, 0.574318, 0.549086],
[0.124395, 0.578002, 0.548287],
[0.123463, 0.581687, 0.547445],
[0.122606, 0.585371, 0.546557],
[0.121831, 0.589055, 0.545623],
[0.121148, 0.592739, 0.544641],
[0.120565, 0.596422, 0.543611],
[0.120092, 0.600104, 0.542530],
[0.119738, 0.603785, 0.541400],
[0.119512, 0.607464, 0.540218],
[0.119423, 0.611141, 0.538982],
[0.119483, 0.614817, 0.537692],
[0.119699, 0.618490, 0.536347],
[0.120081, 0.622161, 0.534946],
[0.120638, 0.625828, 0.533488],
[0.121380, 0.629492, 0.531973],
[0.122312, 0.633153, 0.530398],
[0.123444, 0.636809, 0.528763],
[0.124780, 0.640461, 0.527068],
[0.126326, 0.644107, 0.525311],
[0.128087, 0.647749, 0.523491],
[0.130067, 0.651384, 0.521608],
[0.132268, 0.655014, 0.519661],
[0.134692, 0.658636, 0.517649],
[0.137339, 0.662252, 0.515571],
[0.140210, 0.665859, 0.513427],
[0.143303, 0.669459, 0.511215],
[0.146616, 0.673050, 0.508936],
[0.150148, 0.676631, 0.506589],
[0.153894, 0.680203, 0.504172],
[0.157851, 0.683765, 0.501686],
[0.162016, 0.687316, 0.499129],
[0.166383, 0.690856, 0.496502],
[0.170948, 0.694384, 0.493803],
[0.175707, 0.697900, 0.491033],
[0.180653, 0.701402, 0.488189],
[0.185783, 0.704891, 0.485273],
[0.191090, 0.708366, 0.482284],
[0.196571, 0.711827, 0.479221],
[0.202219, 0.715272, 0.476084],
[0.208030, 0.718701, 0.472873],
[0.214000, 0.722114, 0.469588],
[0.220124, 0.725509, 0.466226],
[0.226397, 0.728888, 0.462789],
[0.232815, 0.732247, 0.459277],
[0.239374, 0.735588, 0.455688],
[0.246070, 0.738910, 0.452024],
[0.252899, 0.742211, 0.448284],
[0.259857, 0.745492, 0.444467],
[0.266941, 0.748751, 0.440573],
[0.274149, 0.751988, 0.436601],
[0.281477, 0.755203, 0.432552],
[0.288921, 0.758394, 0.428426],
[0.296479, 0.761561, 0.424223],
[0.304148, 0.764704, 0.419943],
[0.311925, 0.767822, 0.415586],
[0.319809, 0.770914, 0.411152],
[0.327796, 0.773980, 0.406640],
[0.335885, 0.777018, 0.402049],
[0.344074, 0.780029, 0.397381],
[0.352360, 0.783011, 0.392636],
[0.360741, 0.785964, 0.387814],
[0.369214, 0.788888, 0.382914],
[0.377779, 0.791781, 0.377939],
[0.386433, 0.794644, 0.372886],
[0.395174, 0.797475, 0.367757],
[0.404001, 0.800275, 0.362552],
[0.412913, 0.803041, 0.357269],
[0.421908, 0.805774, 0.351910],
[0.430983, 0.808473, 0.346476],
[0.440137, 0.811138, 0.340967],
[0.449368, 0.813768, 0.335384],
[0.458674, 0.816363, 0.329727],
[0.468053, 0.818921, 0.323998],
[0.477504, 0.821444, 0.318195],
[0.487026, 0.823929, 0.312321],
[0.496615, 0.826376, 0.306377],
[0.506271, 0.828786, 0.300362],
[0.515992, 0.831158, 0.294279],
[0.525776, 0.833491, 0.288127],
[0.535621, 0.835785, 0.281908],
[0.545524, 0.838039, 0.275626],
[0.555484, 0.840254, 0.269281],
[0.565498, 0.842430, 0.262877],
[0.575563, 0.844566, 0.256415],
[0.585678, 0.846661, 0.249897],
[0.595839, 0.848717, 0.243329],
[0.606045, 0.850733, 0.236712],
[0.616293, 0.852709, 0.230052],
[0.626579, 0.854645, 0.223353],
[0.636902, 0.856542, 0.216620],
[0.647257, 0.858400, 0.209861],
[0.657642, 0.860219, 0.203082],
[0.668054, 0.861999, 0.196293],
[0.678489, 0.863742, 0.189503],
[0.688944, 0.865448, 0.182725],
[0.699415, 0.867117, 0.175971],
[0.709898, 0.868751, 0.169257],
[0.720391, 0.870350, 0.162603],
[0.730889, 0.871916, 0.156029],
[0.741388, 0.873449, 0.149561],
[0.751884, 0.874951, 0.143228],
[0.762373, 0.876424, 0.137064],
[0.772852, 0.877868, 0.131109],
[0.783315, 0.879285, 0.125405],
[0.793760, 0.880678, 0.120005],
[0.804182, 0.882046, 0.114965],
[0.814576, 0.883393, 0.110347],
[0.824940, 0.884720, 0.106217],
[0.835270, 0.886029, 0.102646],
[0.845561, 0.887322, 0.099702],
[0.855810, 0.888601, 0.097452],
[0.866013, 0.889868, 0.095953],
[0.876168, 0.891125, 0.095250],
[0.886271, 0.892374, 0.095374],
[0.896320, 0.893616, 0.096335],
[0.906311, 0.894855, 0.098125],
[0.916242, 0.896091, 0.100717],
[0.926106, 0.897330, 0.104071],
[0.935904, 0.898570, 0.108131],
[0.945636, 0.899815, 0.112838],
[0.955300, 0.901065, 0.118128],
[0.964894, 0.902323, 0.123941],
[0.974417, 0.903590, 0.130215],
[0.983868, 0.904867, 0.136897],
[0.993248, 0.906157, 0.143936]]
from matplotlib.colors import ListedColormap
cmaps = {}
cmaps['viridis'] = ListedColormap(_viridis_data, name='viridis')
cmaps['viridis_r'] = ListedColormap(_viridis_data[::-1], name='viridis_r')
viridis = cmaps['viridis']
viridis_r = cmaps['viridis_r']
cm.register_cmap(cmap=viridis)
cm.register_cmap(cmap=viridis_r)
In [3]:
# some functions
def truncated_gauss(N, mu, sigma=0.05, a=0, b=2):
"""Return a N random numbers from a truncated (a,b) Gaussian distribution."""
pos = np.zeros(N)
i = 0
while i<N:
x = gauss(mu, sigma)
#x = np.random.normal(mu,sigma)
if a <= x <= b:
pos[i] = round(x,2)
i += 1
return pos
def get_uniform(N, a=0, b=1):
rs_spatial = RandomState(params['Inhibition']['pos_seed'])
pos = (rs_spatial.rand(N)*a)+(b-a) # a=0.3 and b =0.6 to get values between 0.3 and 0.6, which is between 100 and 300 um
return pos
def get_interneuron():
interneuron = neuron.h.Section()
interneuron.L = 67
interneuron.diam = 67 # so that area is about 14000 um2
interneuron.nseg = 1
interneuron.Ra = 100
interneuron.cm = 1
interneuron.insert('pas')
for seg in interneuron:
seg.pas.g = 0.00015
seg.pas.e = -70
interneuron.insert('hh2')
interneuron.vtraub_hh2 = -55 #resting Vm, BJ was -55
interneuron.gnabar_hh2 = 0.05 #McCormick=15 muS, thal was 0.09
interneuron.gkbar_hh2 = 0.01 #spike duration of interneurons
interneuron.ena = 50
interneuron.ek = -100
return interneuron
def get_netstim(no_reps,freq,kind):
AP_DELAY = 7.5 # approximate time between EPSP onset and AP peak
ns = h.NetStim()
ns.interval = 1000.0/freq
ns.start = WARM_UP-AP_DELAY # ms (most likely) start time of first spike
if kind == "Poisson":
ns.number = 1e9 # (average) number of spikes
ns.noise = 1
elif kind == "deterministic":
ns.number = no_reps # (average) number of spikes
else:
print "kind of input unknown"
return ValueError
return ns
In [4]:
import neuron
from neuron import h
import sys
from ballandstickL5 import *
from numpy.random import RandomState
from random import gauss
import matplotlib.cm as cm
Parameter Settings
In [5]:
params = {
'visual': 'figure',
'results_file': 'pairingscenarios',
'Input':
{
'freq': 10,
'kind': 'deterministic'
},
'Neuron':
{
# morphology
'a_diam':2,
's_diam':18.5,
'd_diam':2,
'd_length':500,
'n_seg':201,
# passive parameters
'R_m':40000,
'R_a':150,
'C_m':0.75,
'E_leak':-70,
'V_rest':-70,
# active conductances
'E_Na':60,
'E_K':-80,
'E_Ca':140,
'g_Na':0.009,
'g_K':0.01,
'g_KA':0.029,
'slope_KA':5,
##calcium
'gsca': 1.5,
'git2': 0.009,
'g_KCa':2.5,
'ifca': False,
# ais
'g_Na_ais':0.3,
'g_Na_ais_shifted':0.3,
'ifshift':True,
'dend_vshift':5
},
'Stimulation':
{
'amp':0.3
},
'Excitation':
{
'freq': 10,
'w_ee': 0.005,
'w_ei': 0.2,
'no_epsps': 8,
'tau1': 0.5,
'tau2': 2
},
'Inhibition':
{
'shunt_reversal':-74,
'pos': 0.4,
'tau1':0.5,
'tau2':5,
'delay':0,
'weight':0.00001,
'timing' : -1,
'random': 'random',
'pos_seed': 6223905,
'seed': 4260404,
'jitter_sigma': 0.5,
'pos_sigma': 0, #0.02
'p_inh':1,
'weight_distribution':'Delta', #'Exponential', 'Beta'
'spatial_distribution': 'Normal'#'Normal'
},
'STDP':
{
'delta_t': 0,
'thresh' : -40,
'shift' : 1.27,
'potentiation': 0.01,
'depression' : 0.01,
'tau_p' : 10,
'tau_d' : 10,
'wmax': 0.01,
'alpha': 0.005,
'rule': 'anti-Hebbian',
'learning_rate': 1,
'no_reps': 100,
'pot_l':0.02,
'pot_r':1.27
},
'sim':
{
'duration' : 1,
},
'plot':
{
'version':1
}
}
In [6]:
NO_INH = 100 # number of inhibitory synapses
NO_REPS = 100 #number of pairings
DT=0.025 # ms, integration time step
POST_AMP = 0.2 # nA, amplitude of current injection to trigger the POST-synaptic spike
WARM_UP=100 # ms
freq = params['Input']['freq']
kind = params['Input']['kind']
delta_t = params['STDP']['delta_t']
learning_rate = params['STDP']['learning_rate']
no_reps = params['STDP']['no_reps']
weight_distribution = params['Inhibition']['weight_distribution']
inh_pos = params['Inhibition']['pos']
pos_sigma = params['Inhibition']['pos_sigma']
sigma = params['Inhibition']['jitter_sigma']
Circuit elements
In [7]:
# create cell
cell = Neuron(params['Neuron'])
# create interneuron
interneuron = get_interneuron()
# excitatory input neuron
ns = get_netstim(no_reps,freq, kind)
Connections
In [8]:
# excitatory synapse
w_ee = params['Excitation']['w_ee']
no_epsps = params['Excitation']['no_epsps']
ex_ex = h.List()
nc_ex = h.List()
for i in range(1,no_epsps+1):
pos = 0.3 + i * 0.075
ex = neuron.h.Exp2Syn(cell.dendrite(pos))
w = w_ee/no_epsps #0.016
ex.tau1 = params['Excitation']['tau1'] # ms rise time
ex.tau2 = params['Excitation']['tau2'] # ms decay time
ex.e = 0 # mV reversal p
# excite postsynaptic cell
nc = h.NetCon(ns,ex,1,0,w)
ex_ex.append(ex)
nc_ex.append(nc)
# excitatory to inhibitory synapse
ex_inh = neuron.h.Exp2Syn(interneuron(0.5))
ex_inh.tau1 = 0.5 # ms rise time
ex_inh.tau2 = 2 # ms decay time
ex_inh.e = 0 # mV reversal p
# excite interneuron
w_ei = params['Excitation']['w_ei']
nc_ex_inh = h.NetCon(ns,ex_inh,1,0,w_ei)
total_time = WARM_UP+no_reps*(1000.0/freq)+100
# plastic inhibitory synapses
exs = h.List()
exnc = h.List()
syns = h.List()
w = []
# weights of inhibitory synapses
w_ie = params['Inhibition']['weight']
if weight_distribution == 'Delta':
inh_weights = w_ie * np.ones(NO_INH)
elif weight_distribution == 'Exponential':
inh_weights = w_ie * np.random.exponential(size = NO_INH)
elif weight_distribution == 'Beta':
inh_weights = 2 * w_ie * np.random.beta(0.5,0.5,size = NO_INH)
elif weight_distribution == 'Normal':
inh_weights = np.random.normal(w_ie,w_ie/2,size = NO_INH)
else:
raise NameError('Option %s does not exist'%weight_distribution)
# timing of inhibitory synapses
timing = np.zeros((NO_INH))
inh_delay = np.zeros((NO_INH))
if params['Inhibition']['random'] == 'random':
rs = RandomState(params['Inhibition']['seed'])
timing = (rs.rand(NO_INH)*10)+1
timing[timing<0] == 0
elif params['Inhibition']['random'] == 'limited':
timing = np.linspace(1,5.0,num=NO_INH)
else:
timing = np.linspace(1,11,num=NO_INH)
# position of inhibitory synapses
if params['Inhibition']['spatial_distribution'] == 'Normal':
inh_poses = truncated_gauss(NO_INH, inh_pos, pos_sigma, a=0, b=1)
elif params['Inhibition']['spatial_distribution'] == 'Uniform':
inh_poses = get_uniform(NO_INH, a=inh_pos-0.1, b=inh_pos+0.1)
else:
raise ValueError
inh_poses.sort()
# set inhibitory synapses
for inh in np.arange(NO_INH):
inh_delay[inh] = timing[inh]-(sigma*3)
if inh_delay[inh]<0:
inh_delay[inh]=0
if not weight_distribution == 'Delta':
if inh_weights[inh]<=0:
inh_weights[inh]=0.0000000001
w_ie = inh_weights[inh]
shift = params['STDP']['shift']
if params['STDP']['rule'] == 'mexican':
if shift >= 0:
syn = h.Mexhat_Inh_STDP(cell.dendrite(inh_poses[inh]))
syn.shift = shift
syn.alpha = params['STDP']['mex_alpha']
else:
syn = h.Mexhat_Inh_STDP_n(cell.dendrite(inh_poses[inh]))
syn.shift = -shift
syn.alpha = params['STDP']['mex_alpha']
elif params['STDP']['rule'] == 'anti-Hebbian':
if shift >= 0:
syn = h.Exp2Syn_Inh_STDP_nobound(cell.dendrite(inh_poses[inh])) #opt2 without bounds
syn.shift = shift
syn.learning_rate = learning_rate
else:
syn = h.Exp2Syn_Inh_STDP_n(cell.dendrite(inh_poses[inh]))
syn.shift = -shift
elif params['STDP']['rule'] == 'cut':
syn = h.Exp2Syn_Inh_STDP_cut_nobound(cell.dendrite(inh_poses[inh])) #opt2 without bounds
syn.shift = shift
syn.cut = params['STDP']['pot_l']
syn.learning_rate = learning_rate
elif params['STDP']['rule'] == 'optimal':
syn = h.Exp2Syn_Inh_STDP_opt2(cell.dendrite(inh_poses[inh])) #opt2 without bounds
syn.pot_l = params['STDP']['pot_l']
syn.pot_r = params['STDP']['pot_r']
syn.learning_rate = learning_rate
elif params['STDP']['rule'] == 'cut_twice':
syn = h.Exp2Syn_Inh_STDP_cuttwice(cell.dendrite(inh_poses[inh])) #opt2 without bounds
syn.shift = shift
syn.cut = params['STDP']['pot_l']
syn.learning_rate = learning_rate
else:
raise ValueError
syn.tau1 = params['Inhibition']['tau1']
syn.tau2 = params['Inhibition']['tau2']
syn.e = params['Inhibition']['shunt_reversal']
syn.thresh = params['STDP']['thresh']
syn.dd = params['STDP']['potentiation']
syn.dp = params['STDP']['depression']
syn.ptau = params['STDP']['tau_p']
syn.dtau = params['STDP']['tau_p']
syn.wmax = params['STDP']['wmax']
syn.mean = sigma*3
syn.std = sigma
syns.append(syn)
# inh_delay may not be negative
exnc.append(h.NetCon(interneuron(0.5)._ref_v, syn,0,inh_delay[inh],w_ie, sec = interneuron))
tvec = h.Vector()
exnc[inh].record(tvec)
wrec = h.Vector()
wrec.record(exnc[inh]._ref_weight[3])
w.append(wrec)
syn_cond = h.List()
for i in np.arange(len(syns)):
syn_cond.append(neuron.h.Vector())
syn_cond[i].record(syns[i]._ref_g)
In [9]:
# recording
trec = h.Vector()
trec.record(h._ref_t)
rec_v = neuron.h.Vector()
rec_v1 = neuron.h.Vector()
rec_v2 = neuron.h.Vector()
rec_v3 = neuron.h.Vector()
rec_v4 = neuron.h.Vector()
rec_v5 = neuron.h.Vector()
rec_v6 = neuron.h.Vector()
rec_v7 = neuron.h.Vector()
rec_v8 = neuron.h.Vector()
rec_v9 = neuron.h.Vector()
rec_v.record(cell.soma(0.5)._ref_v)
rec_v1.record(cell.dendrite(0.1)._ref_v)
rec_v2.record(cell.dendrite(0.2)._ref_v)
rec_v3.record(cell.dendrite(0.3)._ref_v)
rec_v4.record(cell.dendrite(0.4)._ref_v)
rec_v5.record(cell.dendrite(0.5)._ref_v)
rec_v6.record(cell.dendrite(0.6)._ref_v)
rec_v7.record(cell.dendrite(0.7)._ref_v)
rec_v8.record(cell.dendrite(0.8)._ref_v)
rec_v9.record(cell.dendrite(0.9)._ref_v)
vinhrec = h.Vector()
vinhrec.record(interneuron(0.5)._ref_v)
grec = h.Vector()
grec.record(exnc[0]._ref_weight[1])
Out[9]:
Run Simulation
In [10]:
h.dt = DT
h.celsius = 30
h.finitialize(-70)
neuron.run(total_time)
In [11]:
# data collection
sampling_start = WARM_UP+50
sampling_interval = 1000.0/freq
t = np.array(trec)
inh_spikes = np.array(tvec)
v = np.array(rec_v)
v_inh = np.array(rec_v4)
vinh = np.array(vinhrec)
vd = np.array(rec_v9)
g = np.array(grec)
w = np.array(w)
my_rawdata = {}
my_rawdata['timing'] = timing
my_rawdata['v'] = v
my_rawdata['vd'] = vd
sampling_start = int((WARM_UP+50)/DT)
sampling_interval = int((1000.0/freq)/DT)
my_rawdata['w'] = w
my_rawdata['t'] = t
my_rawdata['v_inh'] = v_inh
my_rawdata['vinh'] = vinh
my_rawdata['inh_delay'] = inh_delay
my_rawdata['inh_spikes'] = inh_spikes
my_rawdata['inh_poses'] = inh_poses
my_rawdata['weight_distribution'] = inh_weights
rawdata = {'raw_data':my_rawdata}
Visualization
In [12]:
get_viridis()
mycmap = cm.get_cmap('viridis')
interval = 1000/freq
AP_amp = np.zeros((NO_REPS))
bAP_amp = np.zeros((NO_REPS))
bAP_distal_amp = np.zeros((NO_REPS))
j=0
for i in np.arange(NO_REPS):
start = int(i*interval/DT+interval/DT-100)
end = int(i*interval/DT+interval/DT+100)
bAP = vd[start:end]
AP_amp[j] = np.max(v[start:end])
AP_time = np.argmax(v[start:end])
bAP_amp[j] = np.max(v_inh[start:end])
bAP_distal_amp[j] = np.max(vd[start:end])
j+=1
Figure 2B top
In [13]:
ax = plt.figure()
j=1
for i in [0,NO_REPS-1]:
axis = plt.subplot(1,2,j)
j+=1
plt.plot(t,v,'k',lw=3 ,label = 'AP')
plt.plot(t,vd,color= mycmap(0.4),lw=3, linestyle='dashed', label = 'bAP')
xmin = (i * interval)+ interval-10
xmax = (i * interval)+ interval+10#100
plt.xlim((xmin,xmax))
plt.ylim((-80,60))
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
plt.xlabel("p %d"%(i+1))
if i==0:
axis.spines['bottom'].set_visible(False)
axis.spines['left'].set_position(('outward', 10))
axis.yaxis.set_ticks_position('left')
plt.xticks([])
plt.ylabel("membrane potential [mV]")
else:
axis.spines['bottom'].set_visible(False)
axis.spines['left'].set_visible(False)
plt.xticks([])
plt.yticks([])
axis.legend(prop={'size':15}, frameon = False)
plt.show()
Figure 2B and D
In [14]:
AP_time = (np.argmax(v[20*interval*int(1/DT)+100:21*interval*int(1/DT)+100])+100)*DT
bAP_thresh_crossing1= (v_inh[20*interval*int(1/DT)+100:21*interval*int(1/DT)+100]>-40)
bAP_thresh_crossing1= (v_inh[0*interval*int(1/DT)+100:1*interval*int(1/DT)+100]>-40)
bAP_thresh_crossing2 = np.nonzero(bAP_thresh_crossing1)
try:
bAP_thresh_crossing = (bAP_thresh_crossing2[0][0]+100)*DT
diff = bAP_thresh_crossing - AP_time
except IndexError:
pass
inh_times = inh_spikes[0]+timing
try:
ref_time = bAP_thresh_crossing
except IndexError:
ref_time = AP_time
timing_relative = ref_time-inh_times
timing_range = np.max(timing_relative)-np.min(timing_relative)
In [15]:
sm3 = plt.cm.ScalarMappable(cmap='viridis_r')
sm3.set_array(timing_relative)
fig = plt.figure(figsize = (9,13.5),dpi=80)
ax = plt.subplot(211)
set_axes(ax)
ax.plot(np.arange(len(AP_amp[:])),AP_amp[:],'k-',lw=3, label = 'somatic AP')
ax.plot(np.arange(len(bAP_amp[:])),bAP_amp[:],color = mycmap(0.8), linestyle = 'dotted', lw=3, label = 'bAP at inh')
ax.plot(np.arange(len(bAP_distal_amp[:])),bAP_distal_amp[:],color=mycmap(0.4), linestyle = 'dashed', lw=3, label = 'distal bAP')
plt.xlabel("number of pairings",fontsize = 'xx-large')
plt.ylabel("membrane potential [mV]",fontsize = 'xx-large')
ax.legend(prop={'size':15}, frameon = False)
plt.xticks(np.arange(0,NO_REPS+1,20),np.arange(0,NO_REPS+1,20))
pairingrange = np.arange(len(bAP_distal_amp[:]))
bAP_fail = pairingrange[bAP_distal_amp[:]<-20]
bAP_fail = np.min(bAP_fail)
plt.title("bAP fails at pairing %d"%bAP_fail)
plt.ylim(-80,40)
ax2 = plt.subplot(212)
set_axes(ax2)
for i in np.arange(len(w[:,0])):
c = ((-timing_relative[i]+abs(np.min(timing_relative))))/timing_range
ax2.plot(np.arange(len(w[i,:NO_REPS*interval*int(1/DT)+1])),((w[i,:NO_REPS*interval*int(1/DT)+1]-w[i,0])/w[i,0]),color = mycmap(c))
plt.xticks(np.arange(0,NO_REPS*interval*int(1/DT)+1,interval*int(1/DT)*20),np.arange(0,NO_REPS+1,20))
plt.ylim(-10,1000)
ax2.set_yscale("symlog", linthreshx=1)
plt.xlabel("number of pairings",fontsize = 'xx-large')
plt.ylabel("synaptic weight [nS]",fontsize = 'xx-large')
cb = fig.colorbar(sm3)
cb.set_label('relative timing $[ms]$', fontsize = 28)
plt.show()
Figure 2C
In [16]:
plt.figure()
count = 1
ax = plt.subplot(111)
set_axes(ax)
w_fix = w[:,-1]
w_fix = w_fix*1000
w_fix1 = w_fix[timing_relative<1.27]
w_fix2 = w_fix[timing_relative>=1.27]
w_fix1_wrong = w_fix1[w_fix1<1]
bins=np.histogram(np.hstack((w_fix1,w_fix2)), bins=40)[1] #get the bin edges
p1 = ax.hist(w_fix1, bins, color=mycmap(0.8), edgecolor = 'None', label = 'Delta t < 1.27ms')
p2 = ax.hist(w_fix2, bins, color=mycmap(0), edgecolor = 'None', label = 'Delta t >= 1.27ms')
p1 = ax.hist(w_fix1_wrong, bins, color=mycmap(0.8), edgecolor = 'None')
plt.xlabel("synaptic weight [nS]", fontsize = 'xx-large')
ax.legend(prop={'size':15}, frameon = False)
plt.tight_layout()
plt.show()
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