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Copy pathACC_hom_vs_het_gEE.m
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ACC_hom_vs_het_gEE.m
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%% Model specification
cd /projectnb/crc-nak/sherfey/projects/ACC_simulations/ACC_models
Ne=80; % 4,80 (10,20,40,80,100)
Ni=.25*Ne;
% load model parameters fit to single cell intrinsic property data
ipfile='/projectnb/crc-nak/sherfey/projects/ACC_simulations/rat-ACd-results/rat-ACd-model_cell-intrinsic-properties.mat';
load(ipfile,'simdata','expdata');
% collect values for parameters to make heterogeneous in E-cell population
het={'gAHP','gcan','gcat','gh','gkca','gks','gkdr','gnaf','gpas'};
params=[];
for i=1:numel(het)
params=cat(2,params,simdata.(het{i}));
end
% parameters of the multivariate normal distribution
MU = median(params,1);
SIGMA = cov(params);
% set baseline E-cell parameter values (median) for homogeneous network
parameters={};
for i=1:length(simdata.parameters)
param=simdata.parameters{i};
value=median(simdata.(param)); % mean(simdata.(param));
parameters{end+1}=param;
parameters{end+1}=value*ones(1,Ne); % homogeneous across population
end
% account for default-value discrepancies b/w implementations (my original Papoutsi [PP13] vs Durstewitz [DS02])
aAHP_scale=1; % 1, 1e6
CAF=600*4.6452; % 600, 600*4.6452
cainf = 5e-5; % .05, 5e-5
parameters{end+1}='aAHP_scale';
parameters{end+1}=aAHP_scale;
parameters{end+1}='CAF';
parameters{end+1}=CAF;
parameters{end+1}='cainf';
parameters{end+1}=cainf;
parameters{end+1}='IC_noise';
% set initial condition noise level (IC_noise*rand offsets each cell's gating variable IC)
IC_noise=.25;
parameters{end+1}=IC_noise;
% E-cell mechanisms
mechanisms={'AHP','cadyn','can','cat','h','iks','kdr','kca','naf','nap','pas'};
% generic inputs and state equations
assembly_size=Ne/2;
Ke1=zeros(1,Ne); Ke1(1:assembly_size)=1; % input kernel for first E-cell assembly
Ke2=zeros(1,Ne); Ke2(assembly_size+1:Ne)=1; % input kernel for all other E-cell assemblies
E_input_def={'input(V)=-gAMPA.*(s1(k,:)+s2(k,:)).*(X-EAMPA); monitor input; EAMPA=0; gAMPA=0; onset=50; offset=inf;';
sprintf('s1=getPoissonGating(baseline/2,dcAMPA1,acAMPA1,fAMPA1,phiAMPA1,onset,offset,tauAMPA,T,Npop,%s,kick,ramp_dc_flag1,ramp_ac_flag1);',toString(Ke1));
sprintf('s2=getPoissonGating(baseline/2,dcAMPA2,acAMPA2,fAMPA2,phiAMPA2,onset,offset,tauAMPA,T,Npop,%s,kick,ramp_dc_flag2,ramp_ac_flag2);',toString(Ke2));
'baseline=0; dcAMPA1=0; acAMPA1=0; fAMPA1=0; phiAMPA1=0; tauAMPA=2; kick=1; ramp_dc_flag1=0; ramp_ac_flag1=0; dcAMPA2=0; acAMPA2=0; fAMPA2=0; phiAMPA2=0; ramp_dc_flag2=0; ramp_ac_flag2=0;';
};
I_input_def={'input(V)=-gAMPA.*s1(k,:).*(X-EAMPA); monitor input; EAMPA=0; gAMPA=0; onset=50; offset=inf;';
's1=getPoissonGating(baseline,dcAMPA1,acAMPA1,fAMPA1,phiAMPA1,onset,offset,tauAMPA,T,Npop,ones(1,Npop),kick,ramp_dc_flag1,ramp_ac_flag1);';
'baseline=0; dcAMPA1=0; acAMPA1=0; fAMPA1=0; phiAMPA1=0; tauAMPA=2; kick=1; ramp_dc_flag1=0; ramp_ac_flag1=0;';
};
state_equations='dV/dt=(@current+input(V)+Iapp*(t>onset&t<offset))./Cm; Cm=1; Iapp=0; V(0)=-65;';
tauAMPA=2;
tauNMDA=95;
tauGABA=13; % ms, inhibition decay time constant
% input parameters
onset=100; % 500 ms, stimulus start time
offset=inf; % ms, stimulus stop time
gAMPAe=.001;
enoise=7500; % Hz
dcAMPA1e=0; dcAMPA2e=dcAMPA1e;
acAMPA1e=7500; acAMPA2e=acAMPA1e;
fAMPA1e=0; fAMPA2e=0;
E_input_parameters={'dcAMPA1',dcAMPA1e,'dcAMPA2',dcAMPA2e,'acAMPA1',acAMPA1e,'acAMPA2',acAMPA2e,'fAMPA1',fAMPA1e,'fAMPA2',fAMPA2e,'gAMPA',gAMPAe,'baseline',enoise,'tauAMPA',tauAMPA,'onset',onset,'offset',offset};
gAMPAi=0;
inoise=0;
dcAMPA1i=0;
acAMPA1i=0;
fAMPA1i=0;
I_input_parameters={'dcAMPA1',dcAMPA1i,'acAMPA1',acAMPA1i,'fAMPA1',fAMPA1i,'gAMPA',gAMPAi,'baseline',inoise,'tauAMPA',tauAMPA,'onset',onset,'offset',offset};
% connectivity parameters and kernels
gAMPAee=0; % E->E (<=.1)
gNMDAee=0; % E->E (.1-.2)
gAMPAie=.1; % I->E (.1)
gAMPAii=1; % I->I
if Ne==80
gAMPAei=1; % E->I (.1)
else
gAMPAei=.1; % E->I (.1)
end
Kii=ones(Ni)-eye(Ni);
Kei=ones(Ne,Ni);
Kie=ones(Ni,Ne);
% define assemblies for competition sims:
bwblockee=0; % kernal scaling for connection b/w E-assemblies (i.e., E1->E2 connected with gsyn=gee*bwblockee)
Kee=zeros(Ne,Ne); % E->E, [N_pre x N_post]
% connect E-cells within block
block=ones(assembly_size)-eye(assembly_size);
for i=1:(Ne/assembly_size) % loop over assemblies
ind=(i-1)*assembly_size+(1:assembly_size);
Kee(ind,ind)=block;
end
% connect E-cells between blocks
K12=(1-Kee-eye(Ne)); % connections b/w blocks 1 and 2
Kee=Kee+bwblockee*K12;
%Kee=ones(Ne)-eye(Ne);
% normalize kernels by number of presynaptic connections
Kee=Kee./repmat(max(1,sum(Kee,1)),[size(Kee,1) 1]);
Kei=Kei./repmat(max(1,sum(Kei,1)),[size(Kei,1) 1]);
Kie=Kie./repmat(max(1,sum(Kie,1)),[size(Kie,1) 1]);
Kii=Kii./repmat(max(1,sum(Kii,1)),[size(Kii,1) 1]);
% NETWORK SPECIFICATION
spec=[];
% Populations:
% E-cells (heterogeneous parameters)
spec.populations(1).name='E';
spec.populations(1).size=Ne;
spec.populations(1).equations=[state_equations E_input_def{:}];
spec.populations(1).mechanism_list=mechanisms;
spec.populations(1).parameters={parameters{:},E_input_parameters{:}};
% Wang-Buzsaki interneuron model, 1996
spec.populations(2).name='I';
spec.populations(2).size=Ni;
spec.populations(2).equations=[state_equations I_input_def{:}];
spec.populations(2).mechanism_list={'WB96FSiNa','WB96FSiK','WB96FSileak'};
spec.populations(2).parameters={'Cm',1,'Eleak',-65,'gleak',.1,'gNa',35,'gK',9,I_input_parameters{:}};
% Connections:
% E->E (AMPA,NMDA): 50% assemblies
spec.connections(1).direction='E->E';
spec.connections(1).mechanism_list={'iAMPA','iNMDA'};
spec.connections(1).parameters={'gAMPA',gAMPAee,'netcon',Kee,'tauAMPA',tauAMPA,'tauNMDA',tauNMDA};
% E->I (AMPA,NMDA): all-to-all
spec.connections(2).direction='E->I';
spec.connections(2).mechanism_list={'iAMPA'};
spec.connections(2).parameters={'gAMPA',gAMPAei,'netcon',Kei,'tauAMPA',tauAMPA};
% I->E (GABA): all-to-all
spec.connections(3).direction='I->E';
spec.connections(3).mechanism_list={'iGABA'};
spec.connections(3).parameters={'gGABA',gAMPAie,'netcon',Kie,'tauGABA',tauGABA};
% I->I (GABA): all-to-all
spec.connections(4).direction='I->I';
spec.connections(4).mechanism_list={'iGABA'};
spec.connections(4).parameters={'gGABA',gAMPAii,'netcon',Kii,'tauGABA',tauGABA};
% store baseline model
base=spec;
% limit the number of cores to <= scc limit
maxNumCompThreads(4);
% define random seeds for all simulations (use same for hom and het)
num_seeds=1e3; % <= max_num_realizations
seedfile=sprintf('%gseeds.mat',num_seeds);
if exist(seedfile,'file')
load(seedfile,'seeds');
else
seeds=zeros(num_seeds,1);
for i=1:num_seeds
rng('shuffle');
seeds(i)=getfield(rng,'Seed');
pause(.01);
end
% save seeds to file (for reproducibility)
save(seedfile,'seeds');
end
%fprintf('%i\n',seeds)
% ##############################################
% next: [hom within only]; hom within & between; then het
% input parameters
f1=[20 42.5]; f2=f1; acAMPA1e=4500; enoise=acAMPA1e;
f1=20; f2=42.5; acAMPA1e=4500; enoise=acAMPA1e;
f1=33; f2=37; acAMPA1e=4500; enoise=acAMPA1e;
f1=20; f2=24; acAMPA1e=4500; enoise=acAMPA1e;
tauGABA=5; % 5, 13
gAMPAee=[0 .05]; % E->E (<=.1)
gNMDAee=[0 .1]; % (later: 0,.1,.2)
gAMPAei=1;
vary={'E','fAMPA1',f1;'E','fAMPA2',f2;'E->E','gNMDA',gNMDAee;'E->E','gAMPA',gAMPAee;'E','baseline',enoise;'E','(acAMPA1,acAMPA2)',acAMPA1e;'E','(dcAMPA1,dcAMPA2)',dcAMPA1e;'(I->E,I->I)','tauGABA',tauGABA;'E->I','gAMPA',gAMPAei};
% betweenblockee=0; % kernal scaling for connection b/w E-assemblies (i.e., E1->E2 connected with gsyn=gee*bwblockee)
% withinblockee=1;
betweenblockee=1; % kernal scaling for connection b/w E-assemblies (i.e., E1->E2 connected with gsyn=gee*bwblockee)
withinblockee=1;
% connect E-cells within and between blocks
K11=zeros(Ne,Ne); % E->E, [N_pre x N_post]
block=ones(assembly_size)-eye(assembly_size);
for i=1:(Ne/assembly_size) % loop over assemblies
ind=(i-1)*assembly_size+(1:assembly_size);
K11(ind,ind)=block;
end
K12=(1-K11-eye(Ne)); % connections b/w blocks 1 and 2
Kee=withinblockee*K11+betweenblockee*K12;
Kee=Kee./repmat(max(1,sum(Kee,1)),[size(Kee,1) 1]);
figure; imagesc(Kee)
spec=ApplyModifications(base,{'E->E','netcon',Kee});
% simulator options
tspan=[0 3000]; dt=.01; solver='rk1'; compile_flag=1; downsample_factor=10;
save_data_flag=1; cluster_flag=1; memory_limit='32G'; sims_per_job=1;
analysis_functions=@CalcSpikeSync;
analysis_options={'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]}};
plot_functions={@PlotData,@PlotData};
plot_options={{'plot_type','rastergram'},{'plot_type','power','xlim',[0 100]}};
simulator_options={'save_data_flag',save_data_flag,'tspan',tspan,'solver',solver,'dt',dt,'compile_flag',compile_flag,'verbose_flag',1,'cluster_flag',cluster_flag,'plot_functions',plot_functions,'plot_options',plot_options,'memory_limit',memory_limit,'downsample_factor',downsample_factor,'analysis_functions',analysis_functions,'analysis_options',analysis_options,'sims_per_job',sims_per_job};
rep=1;
study_dir_root=sprintf('studies/sweeps_het-vs-hom_ac%g_EEbetween%g_EEwithin%g_gEEnmda%g-%g_gEEampa%g-%g_gEI%g',acAMPA1e,withinblockee,betweenblockee,gNMDAee(1),gNMDAee(end),gAMPAee(1),gAMPAee(end),gAMPAei);
hom_study_dir=sprintf('%sNe%g_f_%g-%gHz-vs-%g-%gHz_tauI%gms_HOM_%g',study_dir_root,Ne,f1(1),f1(end),f2(1),f2(end),tauGABA,rep);
[data,hom_studyinfo]=SimulateModel(spec,'random_seed',seeds(rep),'study_dir',hom_study_dir,'vary',vary,simulator_options{:});
unix(sprintf('cat %s/pbsout/sim_job1.out',hom_studyinfo.simulations(1).batch_dir));
homdata=ImportData(hom_study_dir);
PlotData(homdata,'plot_type','rastergram');
% het sims
hetdegree=.05; % .2
rng(seeds(rep)); % set the random seed for drawing parameter values
values = mvnrnd(MU,hetdegree*SIGMA,Ne/2); % parameter values for one realization of E-cell population
values = cat(1,values,values); % copy for each assembly
values = max(0,values); % turn off currents w/ negative max conductance
% update model specification with heterogeneous parameter values
mods={};
for i=1:numel(het)
mods=cat(1,mods,{'E',het{i},values(:,i)'});
end
spec=ApplyModifications(spec,mods);
study_dir_root=sprintf('studies/sweeps_het-vs-hom_ac%g_EEbetween%g_EEwithin%g_gEEnmda%g-%g_gEEampa%g-%g_gEI%g',acAMPA1e,withinblockee,betweenblockee,gNMDAee(1),gNMDAee(end),gAMPAee(1),gAMPAee(end),gAMPAei);
het_study_dir=sprintf('%sNe%g_f_%g-%gHz-vs-%g-%gHz_tauI%gms_HET%g_%g',study_dir_root,Ne,f1(1),f1(end),f2(1),f2(end),tauGABA,hetdegree,rep);
[data,het_studyinfo]=SimulateModel(spec,'random_seed',seeds(rep),'study_dir',het_study_dir,'vary',vary,simulator_options{:});
unix(sprintf('cat %s/pbsout/sim_job1.out',het_studyinfo.simulations(1).batch_dir));
hetdata=ImportData(het_study_dir);
PlotData(hetdata,'plot_type','rastergram');
% analysis
maxlag_time=10;
data=SelectData(homdata,'time_limits',[1000 3000]);
homdN=[]; homco=[]; homnco=[]; homsync=[];
for i=1:length(data)
homstats(i)=AnalyzeData(data(i),@CalcSpikeSync,'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]},'maxlag_time',maxlag_time);
homdN=[homdN homstats(i).pairs.dNsumN]; homsync=[homsync homstats(i).pairs.xcsum_pops];
homco=[homco homstats(i).pairs.coactivity]; homnco=[homnco homstats(i).pairs.ncoactivity];
end
[homdN;homsync;homco;homnco]
data=SelectData(hetdata,'time_limits',[1000 3000]);
hetdN=[]; hetco=[]; hetnco=[]; hetsync=[];
for i=1:length(data)
hetstats(i)=AnalyzeData(data(i),@CalcSpikeSync,'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]},'maxlag_time',maxlag_time);
hetdN=[hetdN hetstats(i).pairs.dNsumN]; hetsync=[hetsync hetstats(i).pairs.xcsum_pops];
hetco=[hetco hetstats(i).pairs.coactivity]; hetnco=[hetnco hetstats(i).pairs.ncoactivity];
end
[hetdN;hetsync;hetco;hetnco]
% gAMPAee=[0 .05];
% gNMDAee=[0 .1];
% gAMPA: 0 .05 0 .05
% gNMDA: 0 0 .1 .1
% f1=20; f2=42.5; acAMPA1e=4500;
% (betweenblockee=0, withinblockee=1):
% [homdN;homsync;homco;homnco] =
% 0.5916 0.5285 0.4095 0.2099 homdN
% 0.3753 0.4389 0.4557 0.3237 homsync
% 0.4725 0.5104 0.3838 0.3545 homco
% 0.0112 0.0123 0.0132 0.0061 homnco
% [hetdN;hetsync;hetco;hetnco] =
% 0.1419 0.0800 0.0119 0.0497 hetdN
% 0.2593 0.2144 0.2086 0.1517 hetsync
% 0.4955 0.3714 0.4143 0.4040 hetco
% 0.0112 0.0082 0.0076 0.0053 hetnco
% (betweenblockee=1, withinblockee=1):
% [homdN;homsync;homco;homnco] =
% 0.5916 0.4079 0.2221 0.0749 homdN
% 0.3753 0.4673 0.5306 0.4496 homsync
% 0.4725 0.5421 0.5091 0.4524 homco
% 0.0112 0.0123 0.0141 0.0098 homnco
% [hetdN;hetsync;hetco;hetnco] =
% 0.1419 0.0959 0.0086 0.0569
% 0.2593 0.2284 0.2117 0.2165
% 0.4955 0.3885 0.5177 0.4000
% 0.0112 0.0087 0.0073 0.0074
% f1=33; f2=37; acAMPA1e=4500;
% (betweenblockee=1, withinblockee=1):
% [homdN;homsync;homco;homnco] =
% 0.0425 0.0075 -0.0281 0.0060
% 0.3958 0.4305 0.4917 0.4186
% 0.5306 0.4286 0.5856 0.4891
% 0.0126 0.0137 0.0132 0.0094
% [hetdN;hetsync;hetco;hetnco] =
% -0.0022 -0.0223 -0.0142 0
% 0.2856 0.2417 0.2833 0.3128
% 0.4912 0.5079 0.5120 0.5338
% 0.0120 0.0097 0.0108 0.0095
% f1=20; f2=24; acAMPA1e=4500;
% (betweenblockee=1, withinblockee=1):
% [homdN;homsync;homco;homnco] =
% 0.1839 0.1033 -0.1737 -0.0898
% 0.3758 0.4379 0.4088 0.4489
% 0.3786 0.4074 0.3398 0.2683
% 0.0084 0.0092 0.0105 0.0080
% [hetdN;hetsync;hetco;hetnco] =
% -0.0669 -0.1194 -0.1346 -0.0539
% 0.2620 0.2962 0.2935 0.4513
% 0.4500 0.3866 0.3761 0.4628
% 0.0082 0.0070 0.0073 0.0093
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
f1=20; f2=24; acAMPA1e=4500; enoise=acAMPA1e;
tauGABA=5; % 5, 13
gAMPAee=[0 .05 .1 .15 .2]; % E->E (<=.1)
gNMDAee=[0 .1 .2 .3 .4]; % (later: 0,.1,.2)
gAMPAei=1;
vary={'E','fAMPA1',f1;'E','fAMPA2',f2;'E->E','gNMDA',gNMDAee;'E->E','gAMPA',gAMPAee;'E','baseline',enoise;'E','(acAMPA1,acAMPA2)',acAMPA1e;'E','(dcAMPA1,dcAMPA2)',dcAMPA1e;'(I->E,I->I)','tauGABA',tauGABA;'E->I','gAMPA',gAMPAei};
% simulator options
tspan=[0 3000]; dt=.01; solver='rk1'; compile_flag=1; downsample_factor=10;
save_data_flag=1; cluster_flag=1; memory_limit='32G'; sims_per_job=1;
analysis_functions=@CalcSpikeSync;
analysis_options={'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]}};
plot_functions={@PlotData,@PlotData};
plot_options={{'plot_type','rastergram'},{'plot_type','power','xlim',[0 100]}};
simulator_options={'save_data_flag',save_data_flag,'tspan',tspan,'solver',solver,'dt',dt,'compile_flag',compile_flag,'verbose_flag',1,'cluster_flag',cluster_flag,'plot_functions',plot_functions,'plot_options',plot_options,'memory_limit',memory_limit,'downsample_factor',downsample_factor,'analysis_functions',analysis_functions,'analysis_options',analysis_options,'sims_per_job',sims_per_job};
betweenblockee=.5; % 1, .5 kernal scaling for connection b/w E-assemblies (i.e., E1->E2 connected with gsyn=gee*bwblockee)
withinblockee=1;
% connect E-cells within and between blocks
K11=zeros(Ne,Ne); % E->E, [N_pre x N_post]
block=ones(assembly_size)-eye(assembly_size);
for i=1:(Ne/assembly_size) % loop over assemblies
ind=(i-1)*assembly_size+(1:assembly_size);
K11(ind,ind)=block;
end
K12=(1-K11-eye(Ne)); % connections b/w blocks 1 and 2
Kee=withinblockee*K11+betweenblockee*K12;
Kee=Kee./repmat(max(1,sum(Kee,1)),[size(Kee,1) 1]);
figure; imagesc(Kee)
spec=ApplyModifications(base,{'E->E','netcon',Kee});
rep=1;
study_dir_root=sprintf('studies/sweeps_het-vs-hom_ac%g_EEbetween%g_EEwithin%g_gEEnmda%g-%g_gEEampa%g-%g_gEI%g',acAMPA1e,withinblockee,betweenblockee,gNMDAee(1),gNMDAee(end),gAMPAee(1),gAMPAee(end),gAMPAei);
hom_study_dir=sprintf('%sNe%g_f_%g-%gHz-vs-%g-%gHz_tauI%gms_HOM_%g',study_dir_root,Ne,f1(1),f1(end),f2(1),f2(end),tauGABA,rep);
[data,hom_studyinfo]=SimulateModel(spec,'random_seed',seeds(rep),'study_dir',hom_study_dir,'vary',vary,simulator_options{:});
% het sims
hetdegree=.05; % .2
rng(seeds(rep)); % set the random seed for drawing parameter values
values = mvnrnd(MU,hetdegree*SIGMA,Ne/2); % parameter values for one realization of E-cell population
values = cat(1,values,values); % copy for each assembly
values = max(0,values); % turn off currents w/ negative max conductance
% update model specification with heterogeneous parameter values
mods={};
for i=1:numel(het)
mods=cat(1,mods,{'E',het{i},values(:,i)'});
end
spec=ApplyModifications(spec,mods);
study_dir_root=sprintf('studies/sweeps_het-vs-hom_ac%g_EEbetween%g_EEwithin%g_gEEnmda%g-%g_gEEampa%g-%g_gEI%g',acAMPA1e,withinblockee,betweenblockee,gNMDAee(1),gNMDAee(end),gAMPAee(1),gAMPAee(end),gAMPAei);
het_study_dir=sprintf('%sNe%g_f_%g-%gHz-vs-%g-%gHz_tauI%gms_HET%g_%g',study_dir_root,Ne,f1(1),f1(end),f2(1),f2(end),tauGABA,hetdegree,rep);
[data,het_studyinfo]=SimulateModel(spec,'random_seed',seeds(rep),'study_dir',het_study_dir,'vary',vary,simulator_options{:});
homdata=ImportData(hom_study_dir);
hetdata=ImportData(het_study_dir);
% analysis
maxlag_time=10;
data=SelectData(homdata,'time_limits',[1000 3000]);
homdN=[]; homco=[]; homnco=[]; homsync=[];
for i=1:length(data)
homstats(i)=AnalyzeData(data(i),@CalcSpikeSync,'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]},'maxlag_time',maxlag_time);
homdN=[homdN homstats(i).pairs.dNsumN]; homsync=[homsync homstats(i).pairs.xcsum_pops];
homco=[homco homstats(i).pairs.coactivity]; homnco=[homnco homstats(i).pairs.ncoactivity];
end
[homdN;homsync;homco;homnco]
data=SelectData(hetdata,'time_limits',[1000 3000]);
hetdN=[]; hetco=[]; hetnco=[]; hetsync=[];
for i=1:length(data)
hetstats(i)=AnalyzeData(data(i),@CalcSpikeSync,'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]},'maxlag_time',maxlag_time);
hetdN=[hetdN hetstats(i).pairs.dNsumN]; hetsync=[hetsync hetstats(i).pairs.xcsum_pops];
hetco=[hetco hetstats(i).pairs.coactivity]; hetnco=[hetnco hetstats(i).pairs.ncoactivity];
end
[hetdN;hetsync;hetco;hetnco]
%{
(gb=1, gw=1):
HOM:
0.1839 0.1033 0.0871 0.0129 0
0.3758 0.4379 0.6090 1.3043 2.2545
0.3786 0.4074 0.3839 0.3469 0.4143
0.0084 0.0092 0.0087 0.0139 0.0271
-0.1737 -0.0898 -0.0256 -0.0222 0.0033
0.4088 0.4489 0.3799 0.9840 2.7463
0.3398 0.2683 0.3817 0.4348 0.4200
0.0105 0.0080 0.0053 0.0107 0.0460
-0.0290 -0.0110 -0.0139 0.0159 0.0131
0.3990 0.3040 0.4148 1.6223 2.2410
0.3932 0.3381 0.3971 0.3534 0.3649
0.0077 0.0051 0.0045 0.0170 0.0316
-0.0222 -0.0141 0.0101 0.0120 0.0219
0.3134 0.3189 0.3450 0.4520 1.4103
0.3355 0.4494 0.4145 0.4733 0.4048
0.0050 0.0051 0.0042 0.0049 0.0140
-0.0093 0.0031 0.0192 0 0
0.2453 0.3212 0.3191 3.0910 3.2645
0.3765 0.3812 0.5115 0.6500 0.5000
0.0040 0.0047 0.0044 0.0070 0.0269
HET:
-0.0669 -0.1194 -0.0693 -0.0298 -0.0058
0.2620 0.2962 0.4701 0.5857 0.9545
0.4500 0.3866 0.3806 0.4496 0.4773
0.0082 0.0070 0.0085 0.0084 0.0098
-0.1346 -0.0539 -0.0131 0 0.0166
0.2935 0.4513 0.3478 0.4009 1.3130
0.3761 0.4628 0.3931 0.4551 0.5678
0.0073 0.0093 0.0056 0.0059 0.0129
-0.0409 -0.0010 0.0130 0.0189 0.0103
0.3623 0.3233 0.3265 0.4957 0.9335
0.3504 0.3818 0.4331 0.4172 0.5455
0.0075 0.0061 0.0050 0.0057 0.0106
0.0146 0.0118 0.0076 0.0126 -0.0006
0.3153 0.3339 0.2981 0.5054 0.7692
0.3608 0.4620 0.5145 0.4407 0.4277
0.0053 0.0055 0.0044 0.0053 0.0080
0.0025 0.0183 0.0145 -0.0159 -0.0241
0.3201 0.2358 0.2474 0.4180 2.3345
0.5325 0.4310 0.5238 0.5163 0.4571
0.0056 0.0042 0.0041 0.0045 0.0256
%}
%{
(gb=.5, gw=1):
HOM:
0.1839 0.0295 -0.0052 0.0395 -0.0154
0.3758 0.3650 0.4225 0.0686 0.9631
0.3786 0.3458 0.3017 0.1849 0.1667
0.0084 0.0080 0.0080 0.0009 0.0052
-0.1185 -0.1159 -0.0336 -0.0230 -0.0248
0.4882 0.4232 0.1054 0.0603 1.0556
0.3267 0.3455 0.1923 0.0857 0.1882
0.0099 0.0091 0.0017 0.0002 0.0033
-0.0766 -0.0530 -0.0325 0.0657 0.0145
0.3113 0.2573 0.1223 0.1224 1.1402
0.2984 0.3382 0.1429 0.1020 0.1848
0.0058 0.0039 0.0013 0.0007 0.0059
-0.0302 -0.0018 -0.0031 0.0225 0.0397
0.2644 0.3356 0.1874 0.2095 1.2152
0.3944 0.4416 0.1628 0.1739 0.2444
0.0040 0.0052 0.0014 0.0018 0.0100
-0.0197 0.0146 0.0259 -0.0231 0
0.2893 0.3091 0.2876 0.2801 3.0512
0.4107 0.4364 0.3862 0.0845 0.2821
0.0049 0.0045 0.0036 0.0001 0.0038
HET:
-0.0669 -0.0790 -0.0670 -0.0178 0.0124
0.2620 0.3200 0.3593 0.3152 0.7786
0.4500 0.3250 0.3840 0.3985 0.3985
0.0082 0.0081 0.0071 0.0053 0.0076
-0.1323 -0.0679 0.0252 -0.0063 0.0214
0.3171 0.3949 0.3715 0.3211 0.6681
0.4310 0.4375 0.3793 0.3907 0.3551
0.0086 0.0088 0.0060 0.0051 0.0072
-0.0377 0 0.0260 0.0368 -0.0108
0.3053 0.3555 0.3374 0.3778 1.1255
0.3237 0.4082 0.4500 0.4114 0.4348
0.0061 0.0068 0.0050 0.0058 0.0137
-0.0030 -0.0025 0.0060 0.0100 -0.0111
0.2777 0.3397 0.3481 0.3942 0.2359
0.3721 0.4878 0.5549 0.5032 0.3143
0.0052 0.0057 0.0052 0.0058 0.0028
-0.0042 0.0126 0.0080 -0.0092 -0.0218
0.2912 0.2510 0.2504 0.3112 1.3369
0.4971 0.5167 0.5598 0.5114 0.4485
0.0050 0.0045 0.0044 0.0042 0.0121
%}
% -------------------------------------------------------------------------
f1=20; f2=24; acAMPA1e=4500; enoise=acAMPA1e;
tauGABA=5; % 5, 13
gAMPAee=[0 .05 .1 .15 .2]; % E->E (<=.1)
gNMDAee=[0 .1 .2 .3 .4]; % (later: 0,.1,.2)
gAMPAei=1;
vary={'E','fAMPA1',f1;'E','fAMPA2',f2;'E->E','gNMDA',gNMDAee;'E->E','gAMPA',gAMPAee;'E','baseline',enoise;'E','(acAMPA1,acAMPA2)',acAMPA1e;'E','(dcAMPA1,dcAMPA2)',dcAMPA1e;'(I->E,I->I)','tauGABA',tauGABA;'E->I','gAMPA',gAMPAei};
% simulator options
tspan=[0 3000]; dt=.01; solver='rk1'; compile_flag=1; downsample_factor=10;
save_data_flag=1; cluster_flag=1; memory_limit='32G'; sims_per_job=1;
analysis_functions=@CalcSpikeSync;
analysis_options={'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]}};
plot_functions={@PlotData,@PlotData};
plot_options={{'plot_type','rastergram'},{'plot_type','power','xlim',[0 100]}};
simulator_options={'save_data_flag',save_data_flag,'tspan',tspan,'solver',solver,'dt',dt,'compile_flag',compile_flag,'verbose_flag',1,'cluster_flag',cluster_flag,'plot_functions',plot_functions,'plot_options',plot_options,'memory_limit',memory_limit,'downsample_factor',downsample_factor,'analysis_functions',analysis_functions,'analysis_options',analysis_options,'sims_per_job',sims_per_job};
addpath /projectnb/crc-nak/sherfey/projects/ACC_simulations/ACC_models
spec=base;
spec.connections(1).mechanism_list={'iAMPAee','iNMDAee'};
spec.connections(1).parameters={'tauAMPA',tauAMPA,'tauNMDA',tauNMDA};
m=GenerateModel(spec);
d=SimulateModel(m,'compile_flag',1,'verbose_flag',1,'solver','rk1');
figure; imagesc(d.model.fixed_variables.E_E_iAMPAee_netcon)
vary={'E->E','gw',[0 1];'E->E','gb',[0 1]};
d=SimulateModel(spec,'vary',vary,'compile_flag',1,'verbose_flag',1,'solver','rk1');
figure;
subplot(2,2,1); imagesc(d(1).model.fixed_variables.E_E_iAMPAee_netcon)
subplot(2,2,2); imagesc(d(2).model.fixed_variables.E_E_iAMPAee_netcon)
subplot(2,2,3); imagesc(d(3).model.fixed_variables.E_E_iAMPAee_netcon)
subplot(2,2,4); imagesc(d(4).model.fixed_variables.E_E_iAMPAee_netcon)
% -------------------------------------------------------------------------
addpath /projectnb/crc-nak/sherfey/projects/ACC_simulations/ACC_models % add path to getEE()
f1=20; f2=24; acAMPA1e=4500; enoise=acAMPA1e;
tauGABA=5; % 5, 13
gAMPAee=[.05]; % E->E (<=.1)
gNMDAee=[.2]; % (later: 0,.1,.2)
gAMPAei=1;
betweenblockee=[0 .25 .5 .75 1]; % 1, .5 kernal scaling for connection b/w E-assemblies (i.e., E1->E2 connected with gsyn=gee*bwblockee)
withinblockee=1;
vary={'E->E','gw',withinblockee;'E->E','gb',betweenblockee;'E','fAMPA1',f1;'E','fAMPA2',f2;'E->E','gNMDA',gNMDAee;'E->E','gAMPA',gAMPAee;'E','baseline',enoise;'E','(acAMPA1,acAMPA2)',acAMPA1e;'E','(dcAMPA1,dcAMPA2)',dcAMPA1e;'(I->E,I->I)','tauGABA',tauGABA;'E->I','gAMPA',gAMPAei};
rep=2; % 1:10
% for rep=3:10
% simulator options
tspan=[0 3000]; dt=.01; solver='rk1'; compile_flag=1; downsample_factor=10;
save_data_flag=1; cluster_flag=1; memory_limit='32G'; sims_per_job=1;
analysis_functions=@CalcSpikeSync;
analysis_options={'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]}};
plot_functions={@PlotData,@PlotData};
plot_options={{'plot_type','rastergram'},{'plot_type','power','xlim',[0 100]}};
simulator_options={'save_data_flag',save_data_flag,'tspan',tspan,'solver',solver,'dt',dt,'compile_flag',compile_flag,'verbose_flag',1,'cluster_flag',cluster_flag,'plot_functions',plot_functions,'plot_options',plot_options,'memory_limit',memory_limit,'downsample_factor',downsample_factor,'analysis_functions',analysis_functions,'analysis_options',analysis_options,'sims_per_job',sims_per_job};
spec=base;
spec.connections(1).mechanism_list={'iAMPAee','iNMDAee'};
spec.connections(1).parameters={'tauAMPA',tauAMPA,'tauNMDA',tauNMDA};
study_dir_root=sprintf('studies/sweeps_het-vs-hom_ac%g_EEwithin%g-%g_EEbetween%g-%g_gEEnmda%g-%g_gEEampa%g-%g_gEI%g',acAMPA1e,withinblockee(1),withinblockee(end),betweenblockee(1),betweenblockee(end),gNMDAee(1),gNMDAee(end),gAMPAee(1),gAMPAee(end),gAMPAei);
hom_study_dir=sprintf('%sNe%g_f_%g-%gHz-vs-%g-%gHz_tauI%gms_HOM_%g',study_dir_root,Ne,f1(1),f1(end),f2(1),f2(end),tauGABA,rep);
[data,hom_studyinfo]=SimulateModel(spec,'random_seed',seeds(rep),'study_dir',hom_study_dir,'vary',vary,simulator_options{:});
% het sims
hetdegree=.05; % .2
rng(seeds(rep)); % set the random seed for drawing parameter values
values = mvnrnd(MU,hetdegree*SIGMA,Ne/2); % parameter values for one realization of E-cell population
values = cat(1,values,values); % copy for each assembly
values = max(0,values); % turn off currents w/ negative max conductance
% update model specification with heterogeneous parameter values
mods={};
for i=1:numel(het)
mods=cat(1,mods,{'E',het{i},values(:,i)'});
end
spec=ApplyModifications(spec,mods);
study_dir_root=sprintf('studies/sweeps_het-vs-hom_ac%g_EEwithin%g-%g_EEbetween%g-%g_gEEnmda%g-%g_gEEampa%g-%g_gEI%g',acAMPA1e,withinblockee(1),withinblockee(end),betweenblockee(1),betweenblockee(end),gNMDAee(1),gNMDAee(end),gAMPAee(1),gAMPAee(end),gAMPAei);
het_study_dir=sprintf('%sNe%g_f_%g-%gHz-vs-%g-%gHz_tauI%gms_HET%g_%g',study_dir_root,Ne,f1(1),f1(end),f2(1),f2(end),tauGABA,hetdegree,rep);
[data,het_studyinfo]=SimulateModel(spec,'random_seed',seeds(rep),'study_dir',het_study_dir,'vary',vary,simulator_options{:});
% end
homdata=ImportData(hom_study_dir);
hetdata=ImportData(het_study_dir);
% analysis
maxlag_time=10;
data=SelectData(homdata,'time_limits',[1000 3000]);
homdN=[]; homco=[]; homnco=[]; homsync=[]; clear homstats
for i=1:length(data)
homstats(i)=AnalyzeData(data(i),@CalcSpikeSync,'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]},'maxlag_time',maxlag_time);
homdN=[homdN homstats(i).pairs.dNsumN]; homsync=[homsync homstats(i).pairs.xcsum_pops];
homco=[homco homstats(i).pairs.coactivity]; homnco=[homnco homstats(i).pairs.ncoactivity];
end
[homdN;homsync;homco;homnco]
data=SelectData(hetdata,'time_limits',[1000 3000]);
hetdN=[]; hetco=[]; hetnco=[]; hetsync=[]; clear hetstats
for i=1:length(data)
hetstats(i)=AnalyzeData(data(i),@CalcSpikeSync,'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]},'maxlag_time',maxlag_time);
hetdN=[hetdN hetstats(i).pairs.dNsumN]; hetsync=[hetsync hetstats(i).pairs.xcsum_pops];
hetco=[hetco hetstats(i).pairs.coactivity]; hetnco=[hetnco hetstats(i).pairs.ncoactivity];
end
[hetdN;hetsync;hetco;hetnco]
figure;
subplot(2,1,1); plot(betweenblockee,homsync,'bo-',betweenblockee,hetsync,'ro-'); ylabel('sync'); legend('hom','het');
subplot(2,1,2); plot(betweenblockee,abs(homdN),'bo-',betweenblockee,abs(hetdN),'ro-'); ylabel('|dN|'); legend('hom','het');
xlabel('betweenblockee');
xlims=[1000 3000];
PlotData(homdata,'plot_type','rastergram','xlim',xlims);
PlotData(hetdata,'plot_type','rastergram','xlim',xlims);
% ---------------------------------------------------------
% competition test (f1=20Hz, f2=42.5Hz) --> [RESULT]
% model modifications
spec=base;
spec.connections(1).mechanism_list={'iAMPAee','iNMDAee'};
spec.connections(1).parameters={'tauAMPA',tauAMPA,'tauNMDA',tauNMDA};
% parameter modifications
f1=20; f2=42.5; acAMPA1e=4500; enoise=acAMPA1e;
tauGABA=5; gAMPAee=[.05]; gNMDAee=[.2]; gAMPAei=1;
betweenblockee=0; withinblockee=0; % only varying ratio makes a diffence
rep=2;
vary={'E->E','gw',withinblockee;'E->E','gb',betweenblockee;'E','fAMPA1',f1;'E','fAMPA2',f2;'E->E','gNMDA',gNMDAee;'E->E','gAMPA',gAMPAee;'E','baseline',enoise;'E','(acAMPA1,acAMPA2)',acAMPA1e;'E','(dcAMPA1,dcAMPA2)',dcAMPA1e;'(I->E,I->I)','tauGABA',tauGABA;'E->I','gAMPA',gAMPAei};
% simulation controls
tspan=[0 6000]; dt=.01; solver='rk1'; compile_flag=1; downsample_factor=10;
save_data_flag=0; cluster_flag=0; memory_limit='32G'; sims_per_job=1;
analysis_functions=[]; analysis_options=[];
plot_functions=[]; plot_options=[];
simulator_options={'save_data_flag',save_data_flag,'tspan',tspan,'solver',solver,'dt',dt,'compile_flag',compile_flag,'verbose_flag',1,'cluster_flag',cluster_flag,'plot_functions',plot_functions,'plot_options',plot_options,'memory_limit',memory_limit,'downsample_factor',downsample_factor,'analysis_functions',analysis_functions,'analysis_options',analysis_options,'sims_per_job',sims_per_job};
data=SimulateModel(spec,'random_seed',seeds(rep),'vary',vary,simulator_options{:});
PlotData(data,'plot_type','rastergram');
PlotData(data,'variable','E_nap_h');
dat=SelectData(hetdata,'time_limits',[5000 8000]);
stats=AnalyzeData(dat,@CalcSpikeSync,'ROI_pairs',{'E_V',[0 .5],'E_V',[.5 1]},'maxlag_time',maxlag_time);
stats.pairs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% -------------------------------------------------------------------------
rep=1:5;
hetdegree=[.05 .1 .25 1];
% Batch 1: how sync benefit depends on (f1,f2) (f1-f2 and proximity to fr)
% - relate to dependence of competition on (f1,f2)
f1=10:2.5:60;
f2=f1;
betweenblockee=[0];
withinblockee=[0 1];
% Batch 2: how sync benefit depends on connectivity within & b/w assemblies
% - interpret wrt learning and assembly integration
f1=[20 30 40 50];
f2=[20 30 40 50];
betweenblockee=[0 .25 .5 1]; % 1, .5 kernal scaling for connection b/w E-assemblies (i.e., E1->E2 connected with gsyn=gee*bwblockee)
withinblockee=[0 .25 .5 1];
acAMPA1e=4500;
enoise=acAMPA1e;
tauGABA=5;
gAMPAee=.05;
gNMDAee=.2;
gAMPAei=1;
vary={'E->E','gw',withinblockee;'E->E','gb',betweenblockee;'E','fAMPA1',f1;'E','fAMPA2',f2;'E->E','gNMDA',gNMDAee;'E->E','gAMPA',gAMPAee;'E','baseline',enoise;'E','(acAMPA1,acAMPA2)',acAMPA1e;'E','(dcAMPA1,dcAMPA2)',dcAMPA1e;'(I->E,I->I)','tauGABA',tauGABA;'E->I','gAMPA',gAMPAei};
% mods={'E->E','netcon','Kee./repmat(max(1,sum(Kee,1)),[size(Kee,1) 1])';
% 'E->E','Kee','withinblockee*K11+betweenblockee*K12';
% 'E->E','K11',K12;'E->E','K12',K12;
% 'E->E','withinblockee',1;'E->E','betweenblockee',1};
% spec=ApplyModifications(base,mods);
% mods={'E->E','netcon','getEE(80,gw,1)';'E->E','gw',1};
% spec=ApplyModifications(base,mods);
% betweenblockee=1; % 1, .5 kernal scaling for connection b/w E-assemblies (i.e., E1->E2 connected with gsyn=gee*bwblockee)
% withinblockee=1;
% % connect E-cells within and between blocks
% K11=zeros(Ne,Ne); % E->E, [N_pre x N_post]
% block=ones(assembly_size)-eye(assembly_size);
% for i=1:(Ne/assembly_size) % loop over assemblies
% ind=(i-1)*assembly_size+(1:assembly_size);
% K11(ind,ind)=block;
% end
% K12=(1-K11-eye(Ne)); % connections b/w blocks 1 and 2
% Kee=withinblockee*K11+betweenblockee*K12;
% Kee=Kee./repmat(max(1,sum(Kee,1)),[size(Kee,1) 1]);
% figure; imagesc(Kee)