Example #1
0
        'type': 'fit',
        'param': [Veq, 50.],
        'E': Veq,
        'calctype': 'pas'
    }
}
# initialize a greensTree. Here, all the quantities are stored to compute the GF in the
# frequency domain (algorithm of Koch and Poggio, 1985).
greenstree = morphR.greensTree(morphfile,
                               soma_distr=s_distr,
                               ionc_distr=d_distr,
                               cnodesdistr='all')
# initialize a greensFunctionCalculator using the previously created greensTree. This class
# stores all variables necessary to compute the GF in a format fit for simulation, either
# the plain time domain or with the partial fraction decomposition.
gfcalc = morphR.greensFunctionCalculator(greenstree)
gfcalc.set_impedances_logscale(fmax=7, base=10, num=200)
# Now a list of input locations needs to be defined. For the sparse reformulation, the
# first location needs to be the soma
inlocs = [{
    'node': 1,
    'x': .5,
    'ID': 0
}, {
    'node': 4,
    'x': .5,
    'ID': 1
}, {
    'node': 5,
    'x': .5,
    'ID': 2
Example #2
0
## initialization #####################################################################
## Step 0: initialize the morphology
# Specify the path to an '.swc' file.
morphfile = 'morphologies/ball_and_stick_taper.swc'
# Define the ion channel distributions for dendrites and soma. Here the neuron model is 
# passive.
d_distr = {'L': {'type': 'fit', 'param': [Veq, 50.], 'E': Veq, 'calctype': 'pas'}}
s_distr = {'L': {'type': 'fit', 'param': [Veq, 50.], 'E': Veq, 'calctype': 'pas'}}
# initialize a greensTree. Here, all the quantities are stored to compute the GF in the
# frequency domain (algorithm of Koch and Poggio, 1985).
greenstree = morphR.greensTree(morphfile, soma_distr=s_distr, ionc_distr=d_distr, cnodesdistr='all')
# initialize a greensFunctionCalculator using the previously created greensTree. This class
# stores all variables necessary to compute the GF in a format fit for simulation, either 
# the plain time domain or with the partial fraction decomposition.
gfcalc = morphR.greensFunctionCalculator(greenstree)
gfcalc.set_impedances_logscale(fmax=7, base=10, num=200)
# Now a list of input locations needs to be defined. For the sparse reformulation, the 
# first location needs to be the soma
inlocs = [  {'node': 1, 'x': .5, 'ID': 0}, {'node': 4, 'x': .5, 'ID': 1}, {'node': 5, 'x': .5, 'ID': 2}, 
			{'node': 6, 'x': .5, 'ID': 3}, {'node': 7, 'x': .5, 'ID': 4}, {'node': 8, 'x': .5, 'ID': 5}, 
			{'node': 9, 'x': .5, 'ID': 6}]
## Steps 1,2,3 and 4:
# find sets of nearest neighbours, computes the necessary GF kernels, then computes the
# sparse kernels and then fits the partial fraction decomposition using the VF algorithm.
alphas, gammas, pairs, Ms = gfcalc.kernelSet_sparse(inlocs, FFT=False, kernelconstants=True)
## Step 4 bis: compute the vectors that will be used in the simulation
prep = neurM.preprocessor()
mat_dict_hybrid = prep.construct_volterra_matrices_hybrid(dt, alphas, gammas, K, pprint=False)
## Examples of steps that happen within the kernelSet_sparse function
## Step 1: example to find the nearest neighbours
Example #3
0
def measure_velocity(greenstree,
                     greenstree_pas,
                     node_inds_of_Ranvier,
                     ind_ais,
                     Vth=-20,
                     run_NEURON=False,
                     pprint=False,
                     pplot=False,
                     temp=18.5):
    # parameters
    dt = .025
    tmax = 70.
    V0 = -65.
    inds = [n for n in range(6, 11)]
    number_of_nodes = 8
    # initialize a greens function calculator
    gfcalc = morphR.greensFunctionCalculator(greenstree)
    gfcalc.set_impedances_logscale(fmax=7, base=10, num=200)
    gfcalc_pas = morphR.greensFunctionCalculator(greenstree_pas)
    gfcalc_pas.set_impedances_logscale(fmax=7, base=10, num=200)
    # input locations
    inlocs = [{
        'node': n,
        'x': 0.5,
        'ID': ind
    } for ind, n in enumerate([1] + [ind_ais] + node_inds_of_Ranvier)]
    # integration point conductances
    gs_point, es_point = morphR.get_axon_node_conductances(
        greenstree, node_inds_of_Ranvier, ind_ais)
    # input
    Iclamps = [{
        'ID': 0,
        'x': inlocs[0]['x'],
        'node': 1,
        'delay': 15.,
        'dur': 2.,
        'amp': .5
    }]
    # compute SGF
    alphas, gammas, pairs, Ms = gfcalc_pas.kernelSet_sparse(
        inlocs, FFT=False, kernelconstants=True, pprint=False)
    # preprocessor test
    prep = neurM.preprocessor()
    mat_dict_On = prep.construct_volterra_matrices_On(dt,
                                                      alphas,
                                                      gammas,
                                                      pprint=False)
    sv_dict = prep.construct_ionchannel_matrices(inlocs,
                                                 gs_point,
                                                 es_point,
                                                 temp=temp)
    I_in = prep.construct_current_input_matrix(dt, tmax, inlocs, Iclamps)
    # backwards integration
    axon1 = neurM.axon_vectorized(len(inlocs), sv_dict, mat_dict_On, E_eq=V0)
    result = axon1.run_volterra_back_On(tmax, dt, I_in=I_in)
    if run_NEURON:
        # run neuron neuron
        HHneuron = neurM.NeuronNeuron(greenstree,
                                      dt=dt,
                                      truemorph=False,
                                      factorlambda=10)
        HHneuron.add_Iclamp(Iclamps)
        HHneuron.add_recorder(inlocs)
        Vm = HHneuron.run(tdur=tmax, pprint=True)

    if pplot:
        if run_NEURON:
            pl.plot(Vm['t'], Vm[inds[0]], 'r')
            pl.plot(Vm['t'], Vm[inds[0] + number_of_nodes], 'b')
            pl.plot(Vm['t'], Vm[len(result['Vm']) - 1], 'g')
        pl.plot(result['t'], result['Vm'][inds[0]], 'r--', lw=2)
        pl.plot(result['t'],
                result['Vm'][inds[0] + number_of_nodes],
                'b--',
                lw=2)
        pl.plot(result['t'], result['Vm'][-1], 'g--', lw=2)
        pl.show()

    # compute velocity
    v_list = []
    if run_NEURON: v_list_NEURON = []
    for j in inds:
        # i_rv = node_inds_of_Ranvier[j]
        t1 = threshold_crossing_time(result['Vm'][j], dt, Vth=Vth)
        t2 = threshold_crossing_time(result['Vm'][j + number_of_nodes],
                                     dt,
                                     Vth=Vth)
        node_ind_ranvier = node_inds_of_Ranvier[-1]
        node_ind_myelin = node_ind_ranvier - 1
        node_ranvier = greenstree.tree.get_node_with_index(node_ind_ranvier)
        node_myelin = greenstree.tree.get_node_with_index(node_ind_myelin)
        Dx = number_of_nodes*(node_ranvier.get_content()['impedance'].length + \
                node_myelin.get_content()['impedance'].length) * 1e-2 # m
        Dt = (t2 - t1) * 1e-3  # s
        v_list.append(Dx / Dt)  # m/s
        if run_NEURON:
            t1_ = threshold_crossing_time(Vm[j], dt, Vth=Vth)
            t2_ = threshold_crossing_time(Vm[j + number_of_nodes], dt, Vth=Vth)
            Dt_ = (t2_ - t1_) * 1e-3  # s
            v_list_NEURON.append(Dx / Dt_)

    v_avg = np.mean(np.array(v_list))
    if pprint: print 'velocity= ', v_avg, ' m/s'
    if run_NEURON:
        v_avg_NEURON = np.mean(np.array(v_list_NEURON))
        if pprint: print v_avg_NEURON

    if run_NEURON:
        return v_avg, result, Vm
    else:
        return v_avg
Example #4
0
## initialization ######################################################
morphfile = 'morphologies/stellate_v2.swc'#ball_and_stick_taper.swc'#ball_and_stick_taper.swc'#N19ttwt.CNG.swc'#3y_tree.swc'#neocortical_pyramidv2.swc'#
# greenstree
greenstree_sim = morphR.greensTree(morphfile, soma_distr=s_distr_sim, ionc_distr=distr_sim, pprint=False)
# greens tree
# greenstree_calc = morphR.greensTree(morphfile, soma_distr=s_distr_calc, ionc_distr=distr_calc, pprint=False)
greenstree_calc = copy.deepcopy(greenstree_sim)
snode = greenstree_calc.tree.get_node_with_index(1)
print 'number of dendrites: ', len(snode.get_child_nodes()[2:])
gs_soma = snode.get_content()['physiology'].gs
print gs_soma
print snode.get_content()['physiology'].es
for key in gs_soma.keys():
    gs_soma[key] = 0.
gfcalc = morphR.greensFunctionCalculator(greenstree_calc)
gfcalc.set_impedances_logscale(fmax=7, base=10, num=200)
inlocs = greenstree_calc.distribute_inlocs(num=50, distrtype='random', radius=0.0070)
# inlocs = [{'node': 1, 'x': 0.5, 'ID': 0}, {'node': 18, 'x': 0.6, 'ID': 1}]
# (inlocs, inlocs_2) = greenstree_calc.distribute_inlocs(num=15, distrtype='fromleaf', radius=0.0120, split_radius=0.0050)
# print inlocs
# print inlocs_2
print '\n>>> number of input locations     =    ', len(inlocs)
# print '\n>>> number of input locations avg =    ', len(inlocs_2)
# calculate dendritic length
btst = btstats.BTStats(greenstree_calc.tree)
Ltot = btst.total_length()
print '\n>>> total length                  =    ', Ltot, 'um'
# simulation gfcalc
gfcalc_sim = morphR.greensFunctionCalculator(greenstree_sim)
gfcalc_sim.set_impedances_logscale(fmax=7, base=10, num=2)
def measure_velocity(greenstree, greenstree_pas, node_inds_of_Ranvier, ind_ais, Vth=-20, run_NEURON=False, pprint=False, pplot=False, temp=18.5):
    # parameters
    dt = .025
    tmax = 70.
    V0 = -65.
    inds = [n for n in range(6,11)]
    number_of_nodes = 8
    # initialize a greens function calculator
    gfcalc = morphR.greensFunctionCalculator(greenstree)
    gfcalc.set_impedances_logscale(fmax=7, base=10, num=200)
    gfcalc_pas = morphR.greensFunctionCalculator(greenstree_pas)
    gfcalc_pas.set_impedances_logscale(fmax=7, base=10, num=200)
    # input locations
    inlocs = [{'node': n, 'x': 0.5, 'ID': ind} for ind, n in enumerate([1] + [ind_ais] + node_inds_of_Ranvier)]
    # integration point conductances
    gs_point, es_point = morphR.get_axon_node_conductances(greenstree, node_inds_of_Ranvier, ind_ais)
    # input
    Iclamps = [{'ID': 0, 'x': inlocs[0]['x'], 'node':1, 'delay': 15. , 'dur': 2., 'amp': .5}]
    # compute SGF
    alphas, gammas, pairs, Ms = gfcalc_pas.kernelSet_sparse(inlocs, FFT=False, kernelconstants=True, pprint=False)
    # preprocessor test
    prep = neurM.preprocessor()
    mat_dict_On = prep.construct_volterra_matrices_On(dt, alphas, gammas, pprint=False)
    sv_dict = prep.construct_ionchannel_matrices(inlocs, gs_point, es_point, temp=temp)
    I_in = prep.construct_current_input_matrix(dt, tmax, inlocs, Iclamps)
    # backwards integration
    axon1 = neurM.axon_vectorized(len(inlocs), sv_dict, mat_dict_On, E_eq=V0)
    result = axon1.run_volterra_back_On(tmax, dt, I_in=I_in)
    if run_NEURON:
        # run neuron neuron
        HHneuron = neurM.NeuronNeuron(greenstree, dt=dt, truemorph=False, factorlambda=10)
        HHneuron.add_Iclamp(Iclamps)
        HHneuron.add_recorder(inlocs)
        Vm = HHneuron.run(tdur=tmax, pprint=True)

    if pplot:
        if run_NEURON:
            pl.plot(Vm['t'], Vm[inds[0]], 'r')
            pl.plot(Vm['t'], Vm[inds[0]+number_of_nodes], 'b')
            pl.plot(Vm['t'], Vm[len(result['Vm'])-1], 'g')
        pl.plot(result['t'], result['Vm'][inds[0]], 'r--', lw=2)
        pl.plot(result['t'], result['Vm'][inds[0]+number_of_nodes], 'b--', lw=2)
        pl.plot(result['t'], result['Vm'][-1], 'g--', lw=2)
        pl.show()

    # compute velocity
    v_list = []
    if run_NEURON: v_list_NEURON = []
    for j in inds:
        # i_rv = node_inds_of_Ranvier[j]
        t1 = threshold_crossing_time(result['Vm'][j], dt, Vth=Vth)
        t2 = threshold_crossing_time(result['Vm'][j+number_of_nodes], dt, Vth=Vth)
        node_ind_ranvier = node_inds_of_Ranvier[-1]
        node_ind_myelin = node_ind_ranvier - 1
        node_ranvier = greenstree.tree.get_node_with_index(node_ind_ranvier)
        node_myelin = greenstree.tree.get_node_with_index(node_ind_myelin)
        Dx = number_of_nodes*(node_ranvier.get_content()['impedance'].length + \
                node_myelin.get_content()['impedance'].length) * 1e-2 # m
        Dt = (t2 - t1) * 1e-3 # s
        v_list.append(Dx/Dt) # m/s
        if run_NEURON:
            t1_ = threshold_crossing_time(Vm[j], dt, Vth=Vth)
            t2_ = threshold_crossing_time(Vm[j+number_of_nodes], dt, Vth=Vth)
            Dt_ = (t2_ - t1_) * 1e-3 # s
            v_list_NEURON.append(Dx/Dt_)

    v_avg = np.mean(np.array(v_list))
    if pprint: print 'velocity= ', v_avg, ' m/s'
    if run_NEURON:
        v_avg_NEURON = np.mean(np.array(v_list_NEURON))
        if pprint: print v_avg_NEURON

    if run_NEURON:
        return v_avg, result, Vm
    else:
        return v_avg