Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020)

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Accession:239145
This is the L5 pyramidal tract neuron (L5PT) model from Egger, Narayanan et al., Neuron 2020. It allows investigating how synaptic inputs evoked by different sensory stimuli are integrated by the complex intrinsic properties of L5PTs. The model is constrained by anatomical measurements of the subcellular synaptic input patterns to L5PT neurons, in vivo measurements of sensory-evoked responses of different populations of neurons providing these synaptic inputs, and in vitro measurements constraining the biophysical properties of the soma, dendrites and axon (note: the biophysical model is based on the work by Hay et al., Plos Comp Biol 2011). The model files provided here allow performing simulations and analyses presented in Figures 3, 4 and 5.
Reference:
1 . Egger R, Narayanan RT, Guest JM, Bast A, Udvary D, Messore LF, Das S, de Kock CP, Oberlaender M (2020) Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers Neuron
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Calcium; I h; I M; I K; I Na,t; I Na,p; I K,Ca;
Gap Junctions:
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Sensory processing; Stimulus selectivity; Synaptic Integration;
Implementer(s): Egger, Robert [robert.egger at nyumc.org];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I K; I M; I h; I K,Ca; I Calcium; Gaba; Glutamate;
'''
Created on Apr 28, 2012

@author: regger
'''

#from neuron import h, nrn
import numpy as np
import synapse
from collections import Sequence
import neuron
nrn = neuron.nrn
h = neuron.h

class Cell(object):
    '''
    Cell object providing morphological information
    and hoc interface
    '''
    
    def __init__(self):
        '''
        Constructor:
        
        self.id = None
        
        self.soma = None
        
        TODO: implement trees in python to avoid
        NEURON section stack problems that may occur
        during use of SectionLists
        tree and branches are set up by CellParser
        self.tree = None
        
        branches are all processes attached to soma
        self.branches = {}
        
        structures are all processes with different labels
        (e.g., Dendrite, ApicalDendrite, ApicalTuft, Myelin etc..
        self.structures = {}
        
        simply list of all sections
        self.sections = []
        
        self.synapses = {}
        
        TODO: this should be read from parameter file (e_pas)
        self.E = -70.0
        '''
        self.id = None
        self.soma = None
#        TODO: implement trees in python to avoid
#        NEURON section stack problems that may occur
#        during use of SectionLists
#        tree and branches are set up by CellParser
        self.tree = None
#        branches are all processes attached to soma
        self.branches = {}
#        structures are all processes with different labels
#        (e.g., Dendrite, ApicalDendrite, ApicalTuft, Myelin etc..
        self.structures = {}
#        simply list of all sections
        self.sections = []
        self.synapses = {}
#        TODO: this should be read from parameter file (e_pas)
        self.E = -70.0
        self.changeSynParamDict = {}
    
    def re_init_cell(self, replayMode=False):
        '''re-initialize for next simulation run'''
        for sec in self.sections:
            sec._re_init_vm_recording()
            sec._re_init_range_var_recording()
        for synType in self.synapses.keys():
            for syn in self.synapses[synType]:
                syn.disconnect_hoc_synapse()
            if replayMode:
                self.synapses[synType] = []
    
    def record_range_var(self, var, mech=None):
        for sec in self.sections:
            sec._init_range_var_recording(var, mech)
    
    def distance_between_pts(self, sec1, x1, sec2, x2):
        '''computes path length between to points given by
        location x1 and x2 in sections sec1 and sec2
        or by ptID x1 and x2 and section ID sec1 and sec2.
        for now, use built-in NEURON method distance.
        This should probably be changed at some point in the future
        into something like a look-up table of pair-wise distances,
        because repeated computations for 1000s of synapses are going
        to be very expensive... also, only approximate, since NEURON
        computes distances between centers of segments'''
        if isinstance(sec1, int):
            sec1 = self.sections[sec1]
            sec2 = self.sections[sec2]
            x1 = sec1.relPts(x1)
            x2 = sec2.relPts(x2)
#        set origin
        silent = h.distance(0, x1, sec=sec1)
        return h.distance(x2, sec=sec2)
    
    def distance_to_soma(self, sec, x):
        '''computes path length between soma and point given by
        location x in sections sec or by ptID x and section ID sec'''
#        assume the user knows what they're doing...
        if isinstance(sec, int):
            return self.distance_between_pts(self.soma.secID, 0, sec, x)
        else:
            return self.distance_between_pts(self.soma, 0.0, sec, x)
    
    def max_distance(self, label):
        '''computes maximum path length to soma
        of all branches with the same label'''
        if label == 'Soma':
            return self.soma.L
        maxDist = 0.0
#        set origin to 0 of first branch with this label
        for sec in self.sections:
            if sec.label != label:
                continue
            if sec.parent.label == 'Soma':
                silent = h.distance(0, 0.0, sec=sec)
                break
        for branchSectionList in self.branches[label]:
            for sec in branchSectionList:
                secRef = h.SectionRef(sec=sec)
                if not secRef.nchild():
#                    dist = self.distance_to_soma(sec, 1.0)
                    dist = h.distance(1.0, sec=sec)
                    if dist > maxDist:
                        maxDist = dist
        return maxDist
    
    def add_synapse(self, secID, ptID, ptx, preType='Generic', postType='Generic'):
        if not self.synapses.has_key(preType):
            self.synapses[preType] = []
        newSyn = synapse.Synapse(secID, ptID, ptx, preType, postType)
        newSyn.coordinates = np.array(self.sections[secID].pts[ptID])
        self.synapses[preType].append(newSyn)
        return self.synapses[preType][-1]
    
    def remove_synapses(self, preType=None):
        if preType is None:
            return
#        remove all
        if preType == 'All' or preType == 'all':
            for synType in self.synapses.keys():
                synapses = self.synapses[synType]
                del synapses[:]
                del self.synapses[synType]
            return
#        only one type
        else:
            try:
                synapses = self.synapses[preType]
                del synapses[:]
                del self.synapses[preType]
            except KeyError:
                print 'Synapses of type ' + preType + ' not present on cell'
            return
    
    def change_synapse_parameters(self):
        '''
        Change parameters of synapses during simulation.
        self.changeSynParamDict is dictionary of network parameter sets
        with keys corresponding to event times.
        This allows automatic update of parameter sets
        according to their relative timing.
        '''
        raise NotImplementedError('Synapse parameter change does not work correctly with VecStim!')
#        eventList = self.changeSynParamDict.keys()
#        eventList.sort()
#        print 'Cell %s: event at t = %.2f' % (self.id ,eventList[0])
#        tChange = eventList[0]
#        newParamDict = self.changeSynParamDict.pop(eventList[0])
#        synCnt = 0
#        for synType in newParamDict.keys():
#            print '\tchanging parameters for synapses of type %s' % synType
#            for syn in self.synapses[synType]:
#                synCnt += 1
#                if not syn.is_active():
#                    continue
#                #===============================================================
#                # re-compute release times in case release probability changes
#                #===============================================================
#                preChange = False
#                for t in syn.preCell.spikeTimes:
#                    if t >= tChange:
#                        preChange = True
#                        break
#                if preChange:
#                    changeBin = None
#                    for i in range(len(syn.releaseSite.spikeTimes)):
#                        if syn.releaseSite.spikeTimes[i] >= tChange:
#                            changeBin = i
#                    if changeBin is not None:
#                        print '\tdetermine new release times for synapse %d of type %s' % (synCnt-1, synType)
#                        print '\t\told VecStim: %s' % (syn.releaseSite.spikes)
#                        del syn.releaseSite.spikeTimes[changeBin:]
#                        syn.releaseSite.spikes.play()
#                        syn.releaseSite.spikeVec.resize(0)
#                        prelNew = newParamDict[synType].synapses.releaseProb
#                        newSpikes = []
#                        for t in syn.preCell.spikeTimes:
#                            if t >= tChange:
#                                if np.random.rand() < prelNew:
##                                    syn.releaseSite.append(t)
#                                    newSpikes.append(t)
#                                    syn.releaseSite.spikeTimes.append(t)
#                                    print '\t\tnew release time %.2f' % (t)
#                        if len(newSpikes):
#                            print '\t\told NetCon: %s' % (syn.netcons[0])
#                            print '\t\told NetCon valid: %d' % (syn.netcons[0].valid())
#                            del syn.netcons[0]
##                            syn.netcons = []
#                            print '\t\tcreating new VecStim'
#                            del syn.releaseSite.spikes
#                            syn.releaseSite.spikes = h.VecStim()
#                            print '\t\tupdating SpikeVec:'
#                            del syn.releaseSite.spikeVec
#                            syn.releaseSite.spikeVec = h.Vector(newSpikes)
#                            tRelStr = '\t\t'
#                            for t in syn.releaseSite.spikeVec:
#                                tRelStr += str(t)
#                                tRelStr += ', '
#                            print tRelStr
#                            print '\t\tactivating new VecStim %s' % (syn.releaseSite.spikes)
#                            syn.releaseSite.spikes.play(syn.releaseSite.spikeVec)
#                            print '\t\tupdated VecStim %s with %d new spike times' % (syn.releaseSite.spikes, len(newSpikes))
#                            
#                            newSyn = synapse.Synapse(syn.secID, syn.ptID, syn.x, syn.preCellType, syn.postCellType)
#                            newSyn.coordinates = np.array(self.sections[syn.secID].pts[syn.ptID])
#                            newSyn.weight = syn.weight
#                            newSyn.activate_hoc_syn(syn.releaseSite, syn.preCell, self, newParamDict[synType].synapses.receptors)
#                            
#                            syn.netcons = []
#                            syn.receptors = {}
#                            forget = syn
#                            syn = newSyn
#                            del forget
#                            #===============================================================
#                            # update biophysical parameters and NetCon
#                            #===============================================================
##                            for recepStr in newParamDict[synType].synapses.receptors.keys():
##                                recep = newParamDict[synType].synapses.receptors[recepStr]
##                                hocStr = 'h.'
##                                hocStr += recepStr
##                                hocStr += '(x, sec=hocSec)'
##                                newSyn = eval(hocStr)
##                                del syn.receptors[recepStr]
##                                syn.receptors[recepStr] = newSyn
##                                for paramStr in recep.parameter.keys():
##                                #===========================================================
##                                # try treating parameters as NMODL range variables,
##                                # then as (global) NMODL parameters
##                                #===========================================================
##                                    try:
##                                        valStr = str(recep.parameter[paramStr])
##                                        cmd = 'syn.receptors[\'' + recepStr + '\'].' + paramStr + '=' + valStr
###                                        print 'setting %s for synapse of type %s' % (cmd, synType)
##                                        exec(cmd)
##                                    except LookupError:
##                                        cmd = paramStr + '_' + recepStr + '='
##                                        cmd += str(recep.parameter[paramStr])
###                                        print 'setting %s for synapse of type %s' % (cmd, synType)
##                                        h(cmd)
##                                threshParam = float(recep.threshold)
##                                delayParam = float(recep.delay)
##                                newNetcon = h.NetCon(syn.releaseSite.spikes, syn.receptors[recepStr])
###                                print '\t\told NetCon: %s' % (syn.netcons[0])
###                                print '\t\told NetCon valid: %d' % (syn.netcons[0].valid())
###                                del syn.netcons[0]
###                                syn.netcons = []
##                                syn.netcons = [newNetcon]
##                                print '\t\tnew NetCon: %s' % (syn.netcons[0])
##                                print '\t\tnew NetCon valid: %d' % (syn.netcons[0].valid())
##                                syn.netcons[0].threshold = threshParam
##                                syn.netcons[0].delay = delayParam
##                                if isinstance(recep.weight, Sequence):
##                                    for i in range(len(recep.weight)):
##                                        syn.netcons[0].weight[i] = recep.weight[i]
##                                else:
##                                    syn.netcons[0].weight[0] = recep.weight

class PySection(nrn.Section):
    '''
    Subclass of nrn.Section providing additional geometric
    and mechanism information/ handling methods
    '''
    
    def __init__(self, name=None, cell=None, label=None):
        '''
        structure
        self.label = label
        
        reference to parent section
        self.parent = None
        
        connection point at parent section
        self.parentx = 1.0
        
        bounding box around 3D coordinates
        self.bounds = ()
        
        number of traced 3D coordinates
        self.nrOfPts = 0
        
        list of traced 3D coordinates
        self.pts = []
        
        list of relative position of 3D points along section
        self.relPts = []
        
        list of diameters at traced 3D coordinates
        self.diamList = []
        
        total area of all NEURON segments in this section
        self.area = 0.0
        
        list of center points of segments used during simulation
        self.segPts = []
        
        list of x values corresponding to center of each segment
        for looping akin to the hoc function for(x) (without 0  and 1)
        self.segx = []
        
        list of diameters of all segments
        self.segDiams = []
        
        list of neuron Vectors recording voltage in each compartment
        self.recVList = []
        
        dict of range variables recorded
        self.recordVars = {}
        '''
        if name is None:
            name = ''
        if cell is None:
            nrn.Section.__init__(self)
        else:
            nrn.Section.__init__(self, name=name, cell=cell)
        
        '''structure'''
        self.label = label
        '''reference to parent section'''
        self.parent = None
        '''connection point at parent section'''
        self.parentx = 1.0
        '''bounding box around 3D coordinates'''
        self.bounds = ()
        '''number of traced 3D coordinates'''
        self.nrOfPts = 0
        '''list of traced 3D coordinates'''
        self.pts = []
        '''list of relative position of 3D points along section'''
        self.relPts = []
        '''list of diameters at traced 3D coordinates'''
        self.diamList = []
        '''total area of all NEURON segments in this section'''
        self.area = 0.0
        '''list of center points of segments used during simulation (only for visualization)'''
        self.segPts = []
        '''list of x values corresponding to center of each segment
        for looping akin to the hoc function for(x) (without 0  and 1)'''
        self.segx = []
        '''list of diameters of all segments'''
        self.segDiams = []
        '''list of neuron Vectors recording voltage in each compartment'''
        self.recVList = []
        '''dict of range variables recorded'''
        self.recordVars = {}
    
    def set_3d_geometry(self, pts, diams):
        '''
        invokes NEURON 3D geometry setup
        '''
        if len(pts) != len(diams):
            errStr = 'List of diameters does not match list of 3D points'
            raise RuntimeError(errStr)
        self.pts = pts
        self.nrOfPts = len(pts)
        self.diamList = diams
        
#        silent output in dummy instead of stdout
        dummy = h.pt3dclear(sec=self)
        for i in range(len(self.pts)):
            x, y, z = self.pts[i]
            d = self.diamList[i]
            dummy = h.pt3dadd(x, y, z, d, sec=self)
        
#        not good! set after passive properties have been assigned
#        self.nseg = self.nrOfPts
        
        self._compute_bounds()
        self._compute_relative_pts()
#        execute after nr of segments has been determined
#        self._init_vm_recording()
    
    def set_segments(self, nrOfSegments):
        '''
        Set spatial discretization. should be used
        together with biophysical parameters to produce
        meaningful, yet efficient discretization.
        calls private methods to initialize recordings etc.
        '''
        self.nseg = nrOfSegments
        self._compute_seg_pts()
        self._compute_seg_diameters()
        self._compute_total_area()
#        TODO: find a way to make this more efficient,
#        i.e. allocate memory before running simulation
        self._init_vm_recording()
    
    def _compute_seg_diameters(self):
        '''fill list of diameters of all segments.
        Approximation for visualization purposes only.
        Takes diameter at 3D point closest to segment center.'''
        self.segDiams = []
        for x in self.segx:
            minDist = 1.0
            minID = 0
            for i in range(len(self.relPts)):
                dist = abs(self.relPts[i] - x)
                if dist < minDist:
                    minDist = dist
                    minID = i
            self.segDiams.append(self.diamList[minID])
    
    def _compute_total_area(self):
        '''computes total area of all NEURON segments in this section'''
        area = 0.0
        dx = 1.0/self.nseg
        for i in range(self.nseg):
            x = (i+0.5)*dx
            area += h.area(x, sec=self)
        self.area = area
    
    def _compute_bounds(self):
        pts = self.pts
        xMin, xMax = pts[0][0], pts[0][0]
        yMin, yMax = pts[0][1], pts[0][1]
        zMin, zMax = pts[0][2], pts[0][2]
        for i in range(1,len(pts)):
            if pts[i][0] < xMin:
                xMin = pts[i][0]
            if pts[i][0] > xMax:
                xMax = pts[i][0]
            if pts[i][1] < yMin:
                yMin = pts[i][1]
            if pts[i][1] > yMax:
                yMax = pts[i][1]
            if pts[i][2] < zMin:
                zMin = pts[i][2]
            if pts[i][2] > zMax:
                zMax = pts[i][2]
        self.bounds = xMin, xMax, yMin, yMax, zMin, zMax
    
    def _compute_relative_pts(self):
        self.relPts = [0.0]
        ptLength = 0.0
        pts = self.pts
        for i in range(len(pts)-1):
            pt1, pt2 = np.array(pts[i]), np.array(pts[i+1])
            ptLength += np.sqrt(np.sum(np.square(pt1-pt2)))
            x = ptLength/self.L
            self.relPts.append(x)
#        avoid roundoff errors:
        norm = 1.0/self.relPts[-1]
        for i in range(len(self.relPts)-1):
            self.relPts[i] *= norm
        self.relPts[-1] = 1.0
    
    def _compute_seg_pts(self):
        ''' compute 3D center points of segments
        by approximating section as straight line
        (only for visualization) '''
        if len(self.pts) > 1:
            p0 = np.array(self.pts[0])
            p1 = np.array(self.pts[-1])
            vec = p1 - p0
            dist = np.sqrt(np.dot(vec, vec))
            vec /= dist
#            endpoint stays the same:
            segLength = dist/self.nseg
#            total length stays the same (straightening of dendrite;
#             however this moves branch points):
#            segLength = self.L/self.nseg
            for i in range(self.nseg):
                segPt = p0 + (i+0.5)*segLength*vec
                self.segPts.append(segPt)
                self.segx.append((i+0.5)/self.nseg)
        else:
            self.segPts = [self.pts[0]]
    
    def _init_vm_recording(self):
        ''' set up recording of Vm at every point.
        TODO: recVList[0] should store voltage recorded at
        intermediate node between this and parent segment?'''
#        beginVec = h.Vector()
#        beginVec.record(self(0)._ref_v, sec=self)
#        self.recVList.append(beginVec)
        for seg in self:
            vmVec = h.Vector()
            vmVec.record(seg._ref_v, sec=self)
            self.recVList.append(vmVec)
#        endVec = h.Vector()
#        endVec.record(self(1)._ref_v, sec=self)
#        self.recVList.append(endVec)
    
    def _re_init_vm_recording(self):
        '''resize Vm vectors to 0 to avoid NEURON segfaults'''
        for vec in self.recVList:
            vec.resize(0)
    
    def _re_init_range_var_recording(self):
        '''resize Vm vectors to 0 to avoid NEURON segfaults'''
        for key in self.recordVars.keys():
            for vec in self.recordVars[key]:
                vec.resize(0)
    
    def _init_range_var_recording(self, var, mech=None):
        if mech is None:
            if not var in self.recordVars.keys():
                self.recordVars[var] = []
                for seg in self:
                    vec = h.Vector()
                    hRef = eval('seg._ref_'+var)
                    vec.record(hRef, sec=self)
                    self.recordVars[var].append(vec)
        else:
            if not var in self.recordVars.keys():
                key = mech+'.'+var
                self.recordVars[key] = []
                for seg in self:
                    vec = h.Vector()
                    hRef = eval('seg.'+mech+'._ref_'+var)
                    vec.record(hRef, sec=self)
                    self.recordVars[key].append(vec)


class PointCell(object):
    '''
    simple object for use as spike source
    stores spike times in hoc Vector and numpy array
    requires VecStim to trigger spikes at specified times
    
    initialize with list (iterable) of spike times
    '''
    
    def __init__(self, spikeTimes=None):
        '''
        self.spikeTimes = list(spikeTimes)
        self.spikeVec = h.Vector(spikeTimes)
        self.spikes = h.VecStim()
        
        for use as cell connecting to synapses, not directly to NetCons:
        self.synapseList = None
        '''
        if spikeTimes is not None:
            self.spikeTimes = spikeTimes
            self.spikeVec = h.Vector(spikeTimes)
            self.spikes = h.VecStim()
        else:
            self.spikeTimes = []
            self.spikeVec = h.Vector()
            self.spikes = h.VecStim()
        self.playing = False
        self.synapseList = None
    
    def is_active(self):
        return self.playing
    
    def play(self):
        '''Activate point cell'''
        if self.spikeVec.size() and not self.playing:
            self.spikes.play(self.spikeVec)
            self.playing = True
    
    def append(self, spikeT):
        '''append additional spike time'''
        self.spikeTimes.append(spikeT)
        self.spikeTimes.sort()
        self.spikeVec.append(spikeT)
        self.spikeVec.sort()
        self.playing = True
    
    def compute_spike_train_times(self, interval, noise, start=0.0, stop=-1.0, nSpikes=None):
        '''Activate point cell'''
        self.rand = np.random.RandomState(np.random.randint(123456, 1234567))
        self.spikeInterval = interval
        self.noiseParam = noise
        self.start = start
        self.stop = stop
        
        if self.stop < self.start and nSpikes is None:
            errstr = 'Trying to activate SpikeTrain without number of spikes or t stop parameter!'
            raise RuntimeError(errstr)
        
        if nSpikes is not None:
            lastSpike = 0.0
            for i in range(nSpikes):
                if i == 0:
                    tSpike = self.start + self._next_interval() - self.spikeInterval*(1 - self.noiseParam)
                    if tSpike < 0:
                        tSpike = 0
                else:
                    tSpike = lastSpike + self._next_interval()
                self.append(tSpike)
                lastSpike = tSpike
        elif self.stop > self.start:
            lastSpike = 0.0
            while True:
                if lastSpike == 0:
                    tSpike = self.start + self._next_interval() - self.spikeInterval*(1 - self.noiseParam)
                    if tSpike < 0:
                        tSpike = 0
                else:
                    tSpike = lastSpike + self._next_interval()
                if tSpike > self.stop:
                    break
                self.append(tSpike)
                lastSpike = tSpike
        
#        if self.spikeVec.size() and not self.playing:
#            self.spikes.play(self.spikeVec)
#            self.playing = True
        
    def _next_interval(self):
        if self.noiseParam == 0:
            return self.spikeInterval
        else:
            return (1 - self.noiseParam)*self.spikeInterval + self.noiseParam*self.spikeInterval*self.rand.exponential()
    
    def _add_synapse_pointer(self, synapse):
        if self.synapseList is None:
            self.synapseList = [synapse]
        else:
            self.synapseList.append(synapse)
    
    def turn_off(self):
        '''M. Hines: Note that one can turn off a VecStim without destroying it
        by using VecStim.play() with no args. Turn it back on by
        supplying a Vector arg. Or one could resize the Vector to 0.
        necessary b/c vecevent.mod does not implement ref counting'''
        self.playing = False
        self.spikes.play()
        self.spikeTimes = []
        self.spikeVec.resize(0)


class SpikeTrain(PointCell):
    '''
    DEPRECATED: only still in here in case some old
    dependency turns up that has not been found yet.
    Simple object for use as spike train source.
    Pre-computes spike times according to user-provided
    parameters and plays them as a regular point cell.
    Computation of spike times as in NEURON NetStim.
    '''
    
    def __init__(self):
        PointCell.__init__(self)
        self.rand = np.random.RandomState(np.random.randint(123456, 1234567))
        self.spikeInterval = None
        self.noiseParam = 1.0
        self.start = 0.0
        self.stop = -1.0
        self.playing = False
    
    def set_interval(self, interval):
        self.spikeInterval = interval
    
    def set_noise(self, noise):
        self.noiseParam = noise
    
    def set_start(self, start):
        self.start = start
    
    def set_stop(self, stop):
        self.stop = stop
    
    def is_active(self):
        return self.playing
    
    def compute_spike_times(self, nSpikes=None):
        '''Activate point cell'''
        if self.spikeInterval is None:
            raise RuntimeError('Trying to activate SpikeTrain without mean interval')
        if self.stop < self.start and nSpikes is None:
            errstr = 'Trying to activate SpikeTrain without number of spikes or t stop parameter!'
            raise RuntimeError(errstr)
        
        if nSpikes is not None:
            lastSpike = 0.0
            for i in range(nSpikes):
                if i == 0:
                    tSpike = self.start + self._next_interval() - self.spikeInterval*(1 - self.noiseParam)
                    if tSpike < 0:
                        tSpike = 0
                else:
                    tSpike = lastSpike + self._next_interval()
                self.append(tSpike)
                lastSpike = tSpike
        elif self.stop > self.start:
            lastSpike = 0.0
            while True:
                if lastSpike == 0:
                    tSpike = self.start + self._next_interval() - self.spikeInterval*(1 - self.noiseParam)
                    if tSpike < 0:
                        tSpike = 0
                else:
                    tSpike = lastSpike + self._next_interval()
                if tSpike > self.stop:
                    break
                self.append(tSpike)
                lastSpike = tSpike
        
#        if self.spikeVec.size() and not self.playing:
#            self.spikes.play(self.spikeVec)
#            self.playing = True
        
    def _next_interval(self):
        if self.noiseParam == 0:
            return self.spikeInterval
        else:
            return (1 - self.noiseParam)*self.spikeInterval + self.noiseParam*self.spikeInterval*self.rand.exponential()

class SynParameterChanger():
    def __init__(self, cell, synParam, t):
        self.cell = cell
        self.synParam = synParam
        self.tEvent = t
        self.cell.changeSynParamDict[self.tEvent] = self.synParam
    
    def cvode_event(self):
        h.cvode.event(self.tEvent, self.cell.change_synapse_parameters)