Hippocampal CA3 network and circadian regulation (Stanley et al. 2013)

 Download zip file 
Help downloading and running models
Accession:142104
This model produces the hippocampal CA3 neural network model used in the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. This model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS 2.3 to implement parameter sweeps. The model contains four cell types derived from prior papers. CA3 pyramidal are derived from Traub et al (1991); Basket, stratum oriens (O-LM), and Medial Septal GABAergic (MSG) interneurons are taken from Hajos et al (2004).
Reference:
1 . Stanley DA, Talathi SS, Parekh MB, Cordiner DJ, Zhou J, Mareci TH, Ditto WL, Carney PR (2013) Phase shift in the 24-hour rhythm of hippocampal EEG spiking activity in a rat model of temporal lobe epilepsy. J Neurophysiol 110:1070-86 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus; Medial Septum;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron; Hippocampus septum medial GABAergic neuron;
Channel(s): I Na,t; I A; I K; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS; MATLAB;
Model Concept(s): Epilepsy; Brain Rhythms; Circadian Rhythms;
Implementer(s): Stanley, David A ;
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; GabaA; AMPA; I Na,t; I A; I K; I h; I K,Ca; I Calcium; Gaba; Glutamate;
function [T,V,P,R,cc] = dwtdec(y,n,nivlim)
%
%Automated threshold estimation for NRE algorithm
%
%[THRESH,NIV,BETA,R,C] = dwtdec(y,n);
%
%y: time series (column vector)
%n: level of dwt decomposition (default: full decomposition)
%
%THRESH: returned threshold for pruning and power [theta, beta_p]
%NIV: normalized interpeak interval variances of dwt reconstructions
%BETA: fractional power of dwt reconstructions
%R: reconstructed dwt decomposition time series
%C: coefficients of dwt
%
%
%Osbert Zalay, Nov 2008

leny=numel(y);
if nargin < 2
    n=floor(log2(leny));
    nivlim=5e-4;
end
if nargin < 3
    nivlim=5e-4;
end
if n == 0
    R=emd(y).'; cc=0;
    [m,n]=size(R);
else
    [C,L]=wavedec(y,n,'dmey');
    lenL=numel(L);
    LL=L;
    L=cumsum(L);
    R=zeros(leny,n+1);
    cc=zeros(numel(C),(lenL-1));
    cc(1:L(1),1)=C(1:L(1));
    for i=2:(lenL-1)
        cc((L(i-1)+1):L(i),i)=C((L(i-1)+1):(L(i)));
    end
    for i=1:(n+1)
        R(:,i)=waverec(cc(:,i),LL,'dmey');
    end
end
%a=[n:-1:0];
%scal=2.^a;  %~1/f
%sumscal=sum(scal);
[V P]=sigstats(R);
[v p]=sigstats(y);

P=P./p; %normalize to input signal power
%ind=find(~isnan(V));
ind=find(~isnan(V) & V<1);
sump=sum(P(ind));
vp=V(ind).*P(ind)/sump;
nivthr=mean(vp);
%nivthr=mean(V(ind));
%nivthr=min(V); 
%nivthr=min(V)/v;
%nivthr=min(V)/(n*v);
%nivthr=min(V)/(3*v); %min NIV normalized by original NIV/(1/3)
if nivthr < nivlim
    nivthr=nivlim;
end
%pthr=sum(scal.*P')/sumscal;
%pthr=mean(P)/n;
pthr=mean(P(2:end)); %average just the detail coefficients


%Log scale factor
%log bases
bstn=2; %bstn=10; 
bspn=2; 
%factors
ktn=1/2;
kpn=1;

%TN=log(n)/log(bstn); PN=log(n)/log(bspn);
TN=ktn*log(n)/log(bstn); PN=kpn*log(n)/log(bspn);

%[NIV upper bound, pthr upper bound]
%T=[nivthr pthr];
T=[nivthr/TN pthr/PN];

    
function [v p] = sigstats(r)
[m,n]=size(r);
v=zeros(n,1); p=zeros(n,1);
for i=1:n
    v(i)=niv(r(:,i),1);
    p(i)=sum(r(:,i).^2)/m;
end

function [varint]=niv(s,flt)
x=wkeep(s,round(length(s)*0.9));
lenx=length(x);
interval=zeros(lenx,1);
fltr=[1 1 1]/3; 
x1=x(1); x2=x(lenx); 
for j=1:flt
	c=conv(fltr,x);
	x=c(2:lenx+1);
	x(1)=x1;  
    x(lenx)=x2; 
end
count=0;
keepgoing=1; start=2; k=start;
while (keepgoing) & (k < lenx)
    if x(k-1)<x(k) & x(k+1)<x(k)
        maxind1=k;
        keepgoing=0;
    end
    k=k+1;
    start=k;
end
if (start < lenx)
    for i=start:(lenx-1)
        if x(i-1)<x(i) & x(i+1)<x(i)
            count=count+1;
            maxind2=i;
            interval(count)=maxind2-maxind1;
            maxind1=maxind2;
        end
    end
end
interval=interval(find(interval));
if length(interval)<2
    varint=NaN;
else
    interval=interval./mean(interval);
    varint=var(interval);
end

Loading data, please wait...