Hippocampus CA1 Interneuron Specific 3 (IS3) in vivo-like virtual NN simulations (Luo et al 2020)

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Accession:265523
"Disinhibition is a widespread circuit mechanism for information selection and transfer. In the hippocampus, disinhibition of principal cells is provided by the interneuron-specific interneurons that express the vasoactive intestinal polypeptide (VIP-IS) and innervate selectively inhibitory interneurons. By combining optophysiological experiments with computational models, we determined the impact of synaptic inputs onto the network state-dependent recruitment of VIP-IS cells. We found that VIP-IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, by integrating their intrinsic and synaptic properties into computational models, we predicted recruitment of these cells between the rising phase and peak of theta oscillation and during ripples. Two-photon Ca2+-imaging in awake mice supported in part the theoretical predictions, revealing a significant speed modulation of VIP-IS cells and their preferential albeit delayed recruitment during theta-run epochs, with estimated firing at the rising phase and peak of the theta cycle. However, it also uncovered that VIP-IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of VIP-IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding."
Reference:
1 . Luo X, Guet-McCreight A, Villette V, Francavilla R, Marino B, Chamberland S, Skinner FK, Topolnik L (2020) Synaptic Mechanisms Underlying the Network State-Dependent Recruitment of VIP-Expressing Interneurons in the CA1 Hippocampus. Cereb Cortex [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampal CA1 CR/VIP cell;
Channel(s): I Na,t; I Na,p; I A;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Spatial Navigation; Oscillations; Activity Patterns;
Implementer(s): Guet-McCreight, Alexandre [alexandre.guet.mccreight at mail.utoronto.ca];
Search NeuronDB for information about:  I Na,p; I Na,t; I A; Gaba; Glutamate;
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LuoEtAl2020Code
SWR
SDprox2
NPYfiles
PLOTfiles
IKa.mod *
ingauss.mod *
Ksoma.mod *
Nap.mod *
Nasoma.mod *
vecevent.mod *
init.py *
IS3_M2_Case9StarRevised.hoc *
model_decaytimeinhvec.dat *
model_decaytimevec.dat *
model_dendsectionvec.dat *
model_distvec.dat *
model_minweightinhvec.dat *
model_minweightvec.dat *
model_risetimeinhvec.dat *
model_risetimevec.dat *
PlotResults.py
ranstream.hoc *
SynParamSearch.hoc
vecevent.hoc *
vecevent.ses *
                            
### Test Script for a file found in the SDprox2 results from initial Parallel Simulations
from __future__ import division
import numpy
import matplotlib
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
import scipy
from scipy import signal
from scipy import stats

Case = 'SDprox2_E_COM_I_COM'
EXCSLM = 0
EXCSR = 0
OLMSLM = 0
NGFSLM = 0
IS2SLM = 0
BISSR = 0
IS1SR = 0
EXC = 1
INH = 1
AddTheta = 0
AddSWR = 1
SWREXCSR = numpy.array([0,1,1,1,1,1,1])
SWRBISSR = numpy.array([0,0,1,1,0,0,1])
SWRIS1SR = numpy.array([0,0,0,1,0,0,1])
SWROLMSLM = numpy.array([0,0,0,0,1,2,2])

numExcThetaSyns = 27 # i.e. 3 connections so 9 synapses per connection
numInhThetaSyns = 8 # i.e. 2 connections so 4 synapses per connection
numExcSWRSyns = 27 # Just using the same as for theta
numInhSWRSyns = 8 # Just using the same as for theta
SaveExample = 1
synspikesrandseedvec1 = numpy.array([10,1,89189,76511,23884976]) # Note that I use the first index in future simulations
synspikesrandseedvec2 = numpy.array([15,77716,267826,47246,28])
synlocrandseedvec1 = numpy.array([5,889829,294,76161,299835])
synlocrandseedvec2 = numpy.array([2,26556284,42,189,9817])

# HC Treshold Measurement Values
tstop = 10 # seconds
font_size = 13

Examples = numpy.load('NPYfiles/' + Case + '_ExampleHCModelParams.npy')
ExampleStrings = numpy.load('NPYfiles/' + Case + '_ExampleHCModelStrings.npy')
thetaSynMultiplier = numpy.array([0, 0, 0, 0, 0, 0, 0])
SWRSynMultiplier = 0
tstop = h.tstop/1000
dt = h.dt

for randix in range(0,5):
	x = 0
	randseedsynspikes1 = synspikesrandseedvec1[randix]
	randseedsynspikes2 = synspikesrandseedvec2[randix]
	randseedsynloc1 = synlocrandseedvec1[randix]
	randseedsynloc2 = synlocrandseedvec2[randix]
	if Examples[0][x] == 0:
		print(ExampleStrings[x].decode("utf-8") + ' is empty')
		continue
	for y in range(0,7):
		print('Simulating... ' + str(ExampleStrings[x]) + ' #' + str(y+1))
		ExampleString = ExampleStrings[x].decode("utf-8")
		HCNumber = x
		# Run Simulation of Example
		h.randomize_syns(randseedsynloc1,randseedsynloc2)
		h.f(Examples[0][x],Examples[1][x],Examples[2][x],Examples[3][x],SaveExample,randseedsynspikes1,randseedsynspikes2,AddTheta,INH*numInhThetaSyns*thetaSynMultiplier[y],EXC*numExcThetaSyns*thetaSynMultiplier[y],EXCSLM,EXCSR,OLMSLM,NGFSLM,IS2SLM,BISSR,IS1SR,SWREXCSR[y],SWRBISSR[y],SWRIS1SR[y],SWROLMSLM[y],numExcSWRSyns,numInhSWRSyns,AddSWR) # i.e. same random seeds when comparing runs
		
		HC_Trace = numpy.array(h.recV,dtype=numpy.float)
		voltvec = HC_Trace[1:len(HC_Trace)]
		SWRSynMultiplier = SWRSynMultiplier + 1
		bin_number = 30
		bin_range = (8000,10000)
		
		if y == 0:
			f2, axarr2 = matplotlib.pyplot.subplots(7, sharex=True)
			HC_Trace_Baseline = HC_Trace[10000:100001]
			HC_SpikeTimes_Baseline = numpy.zeros((len(HC_Trace),), dtype=numpy.float)
			for i in range(0,len(h.apctimes)): HC_SpikeTimes_Baseline[int(h.apctimes.x[i]/dt)] = h.apctimes.x[i]
			HC_SpikeTimes_Baseline = HC_SpikeTimes_Baseline[10000:100001]
			HC_SpikeTimes_Baseline2 = numpy.array(h.apctimes,dtype=numpy.float)
			
			axarr2[y].hist(HC_SpikeTimes_Baseline, bins = bin_number, range = bin_range, color = 'b', label = r'Baseline')
			axarr2[y].set_xlim(8000,10000)
			axarr2[y].set_ylim(0,6)
			axarr2[y].spines['right'].set_visible(False)
			axarr2[y].spines['top'].set_visible(False)
			axarr2[y].spines['bottom'].set_visible(False)
			for Tick in axarr2[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr2[y].set_xticks([])
			leg = axarr2[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 1:
			HC_Trace_Rhythm = HC_Trace[10000:100001]
			HC_SpikeTimes_Rhythm = numpy.zeros((len(HC_Trace),), dtype=numpy.float)
			for i in range(0,len(h.apctimes)): HC_SpikeTimes_Rhythm[int(h.apctimes.x[i]/dt)] = h.apctimes.x[i]
			HC_SpikeTimes_Rhythm = HC_SpikeTimes_Rhythm[10000:100001]
			HC_SpikeTimes_Rhythm2 = numpy.array(h.apctimes,dtype=numpy.float)
			
			axarr2[y].hist(HC_SpikeTimes_Rhythm, bins = bin_number, range = bin_range, color = 'r', label = r'CA3')
			axarr2[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr2[y].set_xlim(8000,10000)
			axarr2[y].set_ylim(0,6)
			axarr2[y].spines['right'].set_visible(False)
			axarr2[y].spines['top'].set_visible(False)
			axarr2[y].spines['bottom'].set_visible(False)
			for Tick in axarr2[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr2[y].set_xticks([])
			leg = axarr2[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 2:
			HC_Trace_X2Rhythm = HC_Trace[10000:100001]
			HC_SpikeTimes_X2Rhythm = numpy.zeros((len(HC_Trace),), dtype=numpy.float)
			for i in range(0,len(h.apctimes)): HC_SpikeTimes_X2Rhythm[int(h.apctimes.x[i]/dt)] = h.apctimes.x[i]
			HC_SpikeTimes_X2Rhythm = HC_SpikeTimes_X2Rhythm[10000:100001]
			HC_SpikeTimes_X2Rhythm2 = numpy.array(h.apctimes,dtype=numpy.float)
			
			axarr2[y].hist(HC_SpikeTimes_X2Rhythm, bins = bin_number, range = bin_range, color = 'g', label = r'CA3/SRinh')
			axarr2[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr2[y].set_ylabel('Spike Count',fontsize=font_size-2)
			axarr2[y].set_xlim(8000,10000)
			axarr2[y].set_ylim(0,6)
			axarr2[y].spines['right'].set_visible(False)
			axarr2[y].spines['top'].set_visible(False)
			axarr2[y].spines['bottom'].set_visible(False)
			for Tick in axarr2[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr2[y].set_xticks([])
			leg = axarr2[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 3:
			HC_Trace_X3Rhythm = HC_Trace[10000:100001]
			HC_SpikeTimes_X3Rhythm = numpy.zeros((len(HC_Trace),), dtype=numpy.float)
			for i in range(0,len(h.apctimes)): HC_SpikeTimes_X3Rhythm[int(h.apctimes.x[i]/dt)] = h.apctimes.x[i]
			HC_SpikeTimes_X3Rhythm = HC_SpikeTimes_X3Rhythm[10000:100001]
			HC_SpikeTimes_X3Rhythm2 = numpy.array(h.apctimes,dtype=numpy.float)
			
			axarr2[y].hist(HC_SpikeTimes_X3Rhythm, bins = bin_number, range = bin_range, color = 'c', label = r'CA3/$2 \times$SRinh')
			axarr2[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr2[y].set_xlim(8000,10000)
			axarr2[y].set_ylim(0,6)
			axarr2[y].spines['right'].set_visible(False)
			axarr2[y].spines['top'].set_visible(False)
			axarr2[y].spines['bottom'].set_visible(False)
			for Tick in axarr2[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr2[y].set_xticks([])
			leg = axarr2[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 4:
			HC_Trace_X4Rhythm = HC_Trace[10000:100001]
			HC_SpikeTimes_X4Rhythm = numpy.zeros((len(HC_Trace),), dtype=numpy.float)
			for i in range(0,len(h.apctimes)): HC_SpikeTimes_X4Rhythm[int(h.apctimes.x[i]/dt)] = h.apctimes.x[i]
			HC_SpikeTimes_X4Rhythm = HC_SpikeTimes_X4Rhythm[10000:100001]
			HC_SpikeTimes_X4Rhythm2 = numpy.array(h.apctimes,dtype=numpy.float)
			
			axarr2[y].hist(HC_SpikeTimes_X4Rhythm, bins = bin_number, range = bin_range, color = 'm', label = r'CA3/SLMinh')
			axarr2[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr2[y].set_xlim(8000,10000)
			axarr2[y].set_ylim(0,6)
			axarr2[y].spines['right'].set_visible(False)
			axarr2[y].spines['top'].set_visible(False)
			axarr2[y].spines['bottom'].set_visible(False)
			for Tick in axarr2[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr2[y].set_xticks([])
			leg = axarr2[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 5:
			HC_Trace_X5Rhythm = HC_Trace[10000:100001]
			HC_SpikeTimes_X5Rhythm = numpy.zeros((len(HC_Trace),), dtype=numpy.float)
			for i in range(0,len(h.apctimes)): HC_SpikeTimes_X5Rhythm[int(h.apctimes.x[i]/dt)] = h.apctimes.x[i]
			HC_SpikeTimes_X5Rhythm = HC_SpikeTimes_X5Rhythm[10000:100001]
			HC_SpikeTimes_X5Rhythm2 = numpy.array(h.apctimes,dtype=numpy.float)
			
			axarr2[y].hist(HC_SpikeTimes_X5Rhythm, bins = bin_number, range = bin_range, color = 'y', label = r'CA3/$2 \times$SLMinh')
			axarr2[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr2[y].set_xlim(8000,10000)
			axarr2[y].set_ylim(0,6)
			axarr2[y].spines['right'].set_visible(False)
			axarr2[y].spines['top'].set_visible(False)
			axarr2[y].spines['bottom'].set_visible(False)
			for Tick in axarr2[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr2[y].set_xticks([])
			leg = axarr2[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 6:
			HC_Trace_X6Rhythm = HC_Trace[10000:100001]
			HC_SpikeTimes_X6Rhythm = numpy.zeros((len(HC_Trace),), dtype=numpy.float)
			for i in range(0,len(h.apctimes)): HC_SpikeTimes_X6Rhythm[int(h.apctimes.x[i]/dt)] = h.apctimes.x[i]
			HC_SpikeTimes_X6Rhythm = HC_SpikeTimes_X6Rhythm[10000:100001]
			HC_SpikeTimes_X6Rhythm2 = numpy.array(h.apctimes,dtype=numpy.float)
			
			axarr2[y].hist(HC_SpikeTimes_X6Rhythm, bins = bin_number, range = bin_range, color = 'k', label = r'CA3/$2 \times$(SRinh/SLMinh)')
			axarr2[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr2[y].set_xlim(8000,10000)
			axarr2[y].set_ylim(0,6)
			axarr2[y].spines['right'].set_visible(False)
			axarr2[y].spines['top'].set_visible(False)
			leg = axarr2[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
			
			f2.savefig('PLOTfiles/' + Case + '_Hist_' + str(randix) + '_randix_' + ExampleString + '.pdf', bbox_inches='tight')
			f2.savefig('PLOTfiles/' + Case + '_Hist_' + str(randix) + '_randix_' + ExampleString + '.png', bbox_inches='tight')
		
		timevec = numpy.arange(0,10000.1,0.1)
		if y == 0:
			voltvec_baseline = HC_Trace
			f, axarr = matplotlib.pyplot.subplots(7, sharex=True)
			axarr[y].plot(timevec,voltvec_baseline,'b',label = r'Baseline')
			axarr[y].set_ylim(-85,30)
			axarr[y].set_xlim(8000,10000)
			axarr[y].spines['right'].set_visible(False)
			axarr[y].spines['top'].set_visible(False)
			axarr[y].spines['bottom'].set_visible(False)
			for Tick in axarr[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr[y].set_xticks([])
			leg = axarr[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 1:
			voltvec_X1Rhythm = HC_Trace
			axarr[y].plot(timevec,voltvec_X1Rhythm,'r',label = r'CA3')
			axarr[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr[y].set_ylim(-85,30)
			axarr[y].set_xlim(8000,10000)
			axarr[y].spines['right'].set_visible(False)
			axarr[y].spines['top'].set_visible(False)
			axarr[y].spines['bottom'].set_visible(False)
			for Tick in axarr[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr[y].set_xticks([])
			leg = axarr[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 2:
			voltvec_X2Rhythm = HC_Trace
			axarr[y].plot(timevec,voltvec_X2Rhythm,'g',label = r'CA3/SRinh')
			axarr[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr[y].set_ylim(-85,30)
			axarr[y].set_ylabel('Voltage (mV)',fontsize=font_size-2)
			axarr[y].set_xlim(8000,10000)
			axarr[y].spines['right'].set_visible(False)
			axarr[y].spines['top'].set_visible(False)
			axarr[y].spines['bottom'].set_visible(False)
			for Tick in axarr[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr[y].set_xticks([])
			leg = axarr[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 3:
			voltvec_X3Rhythm = HC_Trace
			axarr[y].plot(timevec,voltvec_X3Rhythm,'c',label = r'CA3/$2 \times$SRinh')
			axarr[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr[y].set_ylim(-85,30)
			axarr[y].set_xlim(8000,10000)
			axarr[y].spines['right'].set_visible(False)
			axarr[y].spines['top'].set_visible(False)
			axarr[y].spines['bottom'].set_visible(False)
			for Tick in axarr[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr[y].set_xticks([])
			leg = axarr[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 4:
			voltvec_X4Rhythm = HC_Trace
			axarr[y].plot(timevec,voltvec_X4Rhythm,'m',label = r'CA3/SLMinh')
			axarr[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr[y].set_ylim(-85,30)
			axarr[y].set_xlim(8000,10000)
			axarr[y].spines['right'].set_visible(False)
			axarr[y].spines['top'].set_visible(False)
			axarr[y].spines['bottom'].set_visible(False)
			for Tick in axarr[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr[y].set_xticks([])
			leg = axarr[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 5:
			voltvec_X5Rhythm = HC_Trace
			axarr[y].plot(timevec,voltvec_X5Rhythm,'y',label = r'CA3/$2 \times$SLMinh')
			axarr[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr[y].set_ylim(-85,30)
			axarr[y].set_xlim(8000,10000)
			axarr[y].spines['right'].set_visible(False)
			axarr[y].spines['top'].set_visible(False)
			axarr[y].spines['bottom'].set_visible(False)
			for Tick in axarr[y].xaxis.get_major_ticks():
				Tick.tick1line.set_visible = Tick.tick2line.set_visible = False
			axarr[y].set_xticks([])
			leg = axarr[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
		elif y == 6:
			voltvec_X6Rhythm = HC_Trace
			axarr[y].plot(timevec,voltvec_X6Rhythm,'k',label = r'CA3/$2 \times$(SRinh/SLMinh)')
			axarr[y].axvline(9000, color='k', linestyle='dashed', linewidth=2)
			axarr[y].set_ylim(-85,30)
			axarr[y].set_xlim(8000,10000)
			axarr[y].set_xlabel('Time (ms)',fontsize=font_size-2)
			axarr[y].spines['right'].set_visible(False)
			axarr[y].spines['top'].set_visible(False)
			leg = axarr[y].legend(loc="upper right", handlelength=0, handletextpad=0, fancybox=True)
			leg.get_frame().set_alpha(1)
			for item in leg.legendHandles:
				item.set_visible(False)
			
			f.savefig('PLOTfiles/' + Case + '_Trace_' + str(randix) + '_randix_' + ExampleString + '.pdf', bbox_inches='tight')
			f.savefig('PLOTfiles/' + Case + '_Trace_' + str(randix) + '_randix_' + ExampleString + '.png', bbox_inches='tight')
		
		if y > 0:
			f3, axarr3 = matplotlib.pyplot.subplots(1)
			if SWREXCSR[y] == 1:
				for j in range(0,int(h.excSWRcount)):
					randind = int(h.EXCrandSRSWR.x[j])
					if randind == -1: randind = 0
					SWRVec = numpy.array([],dtype=numpy.float)
					for q in range(0,len(h.SWRSRexcprespiketrains[randind])): SWRVec = numpy.append(SWRVec,h.SWRSRexcprespiketrains[randind].x[q])
					SWRVec = numpy.array(SWRVec,dtype=numpy.float)
					axarr3.vlines(SWRVec,j+0.75,j+1.25,'g')
			if SWRBISSR[y] == 1:
				for o in range(0,int(h.inhSWRcount)):
					randind = int(h.BISrandSRSWR.x[o])
					if randind == -1: randind = 0
					SWRVec = numpy.array([],dtype=numpy.float)
					for q in range(0,len(h.SWRSRBISprespiketrains[randind])): SWRVec = numpy.append(SWRVec,h.SWRSRBISprespiketrains[randind].x[q])
					SWRVec = numpy.array(SWRVec,dtype=numpy.float)
					axarr3.vlines(SWRVec,o+j+1.75,o+j+2.25,color='orange')
			if SWRIS1SR[y] == 1:
				for p in range(0,int(h.inhSWRcount)):
					randind = int(h.IS1randSRSWR.x[p])
					if randind == -1: randind = 0
					SWRVec = numpy.array([],dtype=numpy.float)
					for q in range(0,len(h.SWRSRIS1prespiketrains[randind])): SWRVec = numpy.append(SWRVec,h.SWRSRIS1prespiketrains[randind].x[q])
					SWRVec = numpy.array(SWRVec,dtype=numpy.float)
					axarr3.vlines(SWRVec,p+o+j+2.75,p+o+j+3.25,color='orange')
			if SWROLMSLM[y] == 1:
				for l in range(0,int(h.inhSWRcount)):
					randind = int(h.OLMrandSLMSWR.x[l])
					if randind == -1: randind = 0
					SWRVec = numpy.array([],dtype=numpy.float)
					for q in range(0,len(h.SWRSLMOLMprespiketrains[randind])): SWRVec = numpy.append(SWRVec,h.SWRSLMOLMprespiketrains[randind].x[q])
					SWRVec = numpy.array(SWRVec,dtype=numpy.float)
					axarr3.vlines(SWRVec,l+j+1.75,l+j+2.25,'m')
			if SWROLMSLM[y] == 2:
				for l in range(0,int(h.inhSWRcount)*2):
					randind = int(h.OLMrandSLMSWR.x[l])
					if randind == -1: randind = 0
					SWRVec = numpy.array([],dtype=numpy.float)
					for q in range(0,len(h.SWRSLMOLMprespiketrains[randind])): SWRVec = numpy.append(SWRVec,h.SWRSLMOLMprespiketrains[randind].x[q])
					SWRVec = numpy.array(SWRVec,dtype=numpy.float)
					if SWRIS1SR[y]+SWRBISSR[y] == 2:
						axarr3.vlines(SWRVec,l+p+o+j+3.75,l+p+o+j+4.25,'m')
					elif SWRIS1SR[y]+SWRBISSR[y] < 2:
						axarr3.vlines(SWRVec,l+j+1.75,l+j+2.25,'m')
			axarr3.set_xlim(8990,9100)
			if SWREXCSR[y] == 1 and SWRBISSR[y] == 0 and SWRIS1SR[y] == 0 and SWROLMSLM[y] == 0:
				axarr3.set_ylim(0,j+2)
			if SWREXCSR[y] == 1 and SWRBISSR[y] == 1 and SWRIS1SR[y] == 0 and SWROLMSLM[y] == 0:
				axarr3.set_ylim(0,o+j+3)
			if SWREXCSR[y] == 1 and SWRBISSR[y] == 1 and SWRIS1SR[y] == 1 and SWROLMSLM[y] == 0:
				axarr3.set_ylim(0,p+o+j+4)
			if SWREXCSR[y] == 1 and SWRBISSR[y] == 0 and SWRIS1SR[y] == 0 and SWROLMSLM[y] == 1:
				axarr3.set_ylim(0,l+j+3)
			if SWREXCSR[y] == 1 and SWRBISSR[y] == 0 and SWRIS1SR[y] == 0 and SWROLMSLM[y] == 2:
				axarr3.set_ylim(0,l+j+3)
			if SWREXCSR[y] == 1 and SWRBISSR[y] == 1 and SWRIS1SR[y] == 1 and SWROLMSLM[y] > 0:
				axarr3.set_ylim(0,p+o+l+j+5)
			axarr3.set_xlabel('Time (ms)',fontsize=font_size-4)
			axarr3.set_ylabel('Synapse Number',fontsize=font_size-4)
			f3.savefig('PLOTfiles/' + Case + '_SWRPresynapticSpikes_' + ExampleString + '_X' + str(y) + '_SWRMultiplier.pdf', bbox_inches='tight')
			f3.savefig('PLOTfiles/' + Case + '_SWRPresynapticSpikes_' + ExampleString + '_X' + str(y) + '_SWRMultiplier.png', bbox_inches='tight')
	pyplot.gcf().clear()
	pyplot.cla()
	pyplot.clf()
	pyplot.close()
	pyplot.gcf().clear()
	pyplot.cla()
	pyplot.clf()
	pyplot.close()
	pyplot.gcf().clear()
	pyplot.cla()
	pyplot.clf()
	pyplot.close()
	
	bin_number = 1
	bin_range = (9000,9050)
	
	heights1,bins1 = numpy.histogram(HC_SpikeTimes_Baseline,bins=bin_number,range=bin_range)
	heights2,bins2 = numpy.histogram(HC_SpikeTimes_Rhythm,bins=bin_number,range=bin_range)
	heights3,bins3 = numpy.histogram(HC_SpikeTimes_X2Rhythm,bins=bin_number,range=bin_range)
	heights4,bins4 = numpy.histogram(HC_SpikeTimes_X3Rhythm,bins=bin_number,range=bin_range)
	heights5,bins5 = numpy.histogram(HC_SpikeTimes_X4Rhythm,bins=bin_number,range=bin_range)
	heights6,bins6 = numpy.histogram(HC_SpikeTimes_X5Rhythm,bins=bin_number,range=bin_range)
	heights7,bins7 = numpy.histogram(HC_SpikeTimes_X6Rhythm,bins=bin_number,range=bin_range)
	
	ind = numpy.arange(7)
	width = 0.4
	f4, axarr4 = matplotlib.pyplot.subplots(1, sharex=True)
	axarr4.bar(ind[0]+width, heights1, width, color='b')
	axarr4.bar(ind[1]+width, heights2, width, color='r')
	axarr4.bar(ind[2]+width, heights3, width, color='g')
	axarr4.bar(ind[3]+width, heights4, width, color='c')
	axarr4.bar(ind[4]+width, heights5, width, color='m')
	axarr4.bar(ind[5]+width, heights6, width, color='y')
	axarr4.bar(ind[6]+width, heights7, width, color='k')
	axarr4.set_xticks(ind+width)
	axarr4.set_xticklabels((r'Base',r'CA3',r'CA3/SRinh',r'CA3/$2 \times$SRinh',r'CA3/SLMinh',r'CA3/$2 \times$SLMinh',r'CA3/$2 \times$(SRinh/SLMinh)'),fontsize=font_size-3, rotation=45)
	axarr4.set_ylabel('Spike Count During SWR')
	axarr4.set_xlim(0,7+width)
	pyplot.tight_layout()
	f4.savefig('PLOTfiles/' + Case + '_SWRSpikeCount_' + str(randix) + '_randix_' + ExampleString + '.pdf', bbox_inches='tight')
	f4.savefig('PLOTfiles/' + Case + '_SWRSpikeCount_' + str(randix) + '_randix_' + ExampleString + '.png', bbox_inches='tight')
	pyplot.gcf().clear()
	pyplot.cla()
	pyplot.clf()
	pyplot.close()

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