Neural model of frog ventilatory rhythmogenesis (Horcholle-Bossavit and Quenet 2009)

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Accession:123987
"In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed by lung ventilation episodes. The ventilatory activity results from the interaction of two hypothesized oscillators in the brainstem. Here, we model these oscillators with two coupled neural networks, whose co-activation results in the emergence of new dynamics. .. The biological interest of this formal model is illustrated by the persistence of the relevant dynamical features when perturbations are introduced in the model, i.e. dynamic noises and architecture modifications. The implementation of the networks with clock-driven continuous time neurones provides simulations with physiological time scales."
Reference:
1 . Horcholle-Bossavit G, Quenet B (2009) Neural model of frog ventilatory rhythmogenesis. Biosystems 97:35-43 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Temporal Pattern Generation; Oscillations; Synchronization;
Implementer(s):
%Biosystems. 2009 Jul;97(1):35-43.
%Horcholle-Bossavit G, Quenet B.
%Neural model of frog ventilatory rhythmogenesis.

%global nirings seedscalevol Init
%global vpos1 vpos2 Tsim

matdep =[ 0  0   -1  
          1  0   -1   
          0  1  0 ];                %single loop
Rdep=[1 0 0]; 
mattoub=Boucladj(matdep,Rdep', 5, [4,2],1, [2,5], -1);  
S=mattoub(:,1:end-1);
R=mattoub(:,end);
R=R';
groupex=find(sum(S)>0);             %group of excitatory neurons
groupin=find(sum(S)<0);             %group of inhibitory neurons

Ne=length(groupex);     Ni=length(groupin);
th=[0.5*ones(Ne,1); 	0.5*ones(Ni,1)];
retards=ones(Ne+Ni);                %delay matrix

seedscalevol=3760;
randn('seed',seedscalevol)     
rand('seed',seedscalevol)
Init=zeros(Ne+Ni,1);
type=(sign(sum(S)))';
nirings=[zeros(sum(Init),1),find(Init==1),type((Init==1))]; % neuron states
hstock=zeros(Ni+Ne,Tsim+1);        %potential matrix
hstock(:,1)=Init;
matact=zeros(Ni+Ne,Tsim+1);         %binary activity matrix
matact(:,1)=Init;
epsilon=0;                          %dynamical noise parameter


Tomax=max(max(retards));
for t=1:Tsim
    feu=[];
    matfiltre=zeros(Ne+Ni);
    h=R';
    if ~isempty(nirings)
       for ret=1:max(max(retards))
           fincre=zeros(Ne+Ni);                           
           inter=find(nirings(:,1)==t-ret);               
           feu=[feu; ret+0*inter,inter];                  
           finter=[[nirings(inter,2)]', Ne+Ni+1];          
           fincre(1:Ne+Ni,finter)=1;                       
           fincre=fincre(:,1:Ne+Ni);                       
           matinter=(retards==ret);                        
           matfiltre=matfiltre+((matinter==fincre)&(matinter>0));   
       end
       tfire=nirings(feu(:,2),1);                          
       firedt=nirings(feu(:,2),2);                        
       st=sort(firedt);                                    
       if ~isempty(st)                                    
           dst=diff(st);                                   
           y=st(dst>0);
           firedtone=[y;st(end)];                          
           Sfiltre=S.*matfiltre;                           
           h=h+sum(Sfiltre(:,firedtone),2);                
       end
                             
    end
    randh=randn(Ne+Ni,1);
    hent=h+epsilon*randh-th;                                                                                 
    hstock(:,t+1)=hent;
    n=(hent>0);                                                
    fired=find(n>0);
    matact(:,t+1)=n;                                      
    if ~isempty(fired);
        nirings=[nirings; t+0*fired,fired,type(fired)];                 
    end
end

actot=sum(matact(groupex,:));
subplot('position',vpos1)
set(gcf,'Color',[1 1 1])
plot(nirings((nirings(:,3)>=0),1), nirings((nirings(:,3)>=0),2),'diamond','MarkerEdgeColor',[0 0 0],'MarkerFaceColor',[0 0 0], 'MarkerSize', 2 )
hold on
plot(nirings((nirings(:,3)<0),1), nirings((nirings(:,3)<0),2),'o','MarkerEdgeColor',[0.5 0.5 0.5],'MarkerFaceColor',[0.5 0.5 0.5], 'MarkerSize',1 )
set(gca,'XLim', [0 Tsim],'YLim',[0 Ne+Ni+1]);
set(gca,'YDir','reverse')
set(gca,'FontName','arial','FontWeight','bold','FontSize',9)
text(10,-2,['MCP model   <-    neurone activities    ->   Izhikevich model'],...
    'VerticalAlignment','middle',...
   'HorizontalAlignment','left',...
    'FontName','arial','FontWeight','bold','FontSize',9,'FontAngle','normal','Color',[0 0 0])
    box off
subplot('position',vpos2)
plot([0:Tsim],actot,'k');
set(gca,'XLim', [0 Tsim],'YLim',[1 4]);
set(gca,'FontName','arial','FontWeight','bold','FontSize',9)
xlabel('time steps');
text(2,4,['number of active excitatory neurones     '],...
    'VerticalAlignment','middle',...
   'HorizontalAlignment','left',...
    'FontName','arial','FontWeight','bold','FontSize',9,'FontAngle','normal','Color',[0 0 0])
box off