Cholinergic and nicotinic regulation of DA neuron firing (Morozova et al 2020)

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Accession:266419
The model describes the modulation of firing properties of DA neurons by acetylcholine (ACh) and nicotine in 5 cases: knock-out of ß2-containing nAChRs, ß2-containing nAChRs only on DA neurons, the nAChRs only on GABA neurons, the nAChRs on both DA and GABA neurons and “wild” type (the AChRs on DA, GABA and Glu neurons). The distinct responses to ACh and nicotine could be explained by distinct temporal patterns of these inputs: pulsatile vs. continuous.
Reference:
1 . Morozova E, Faure P, Gutkin B, Lapish C, Kuznetsov A (2020) Distinct temporal structure of nicotinic ACh receptor activation determines responses of VTA neurons to endogenous ACh and nicotine eNeuro, accepted
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Ventral tegmental area dopamine neuron; Ventral tegmental area GABA neuron ;
Channel(s): I K,Ca; I Calcium; I K;
Gap Junctions:
Receptor(s): Cholinergic Receptors;
Gene(s):
Transmitter(s): Acetylcholine; Gaba; Dopamine; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Bursting;
Implementer(s): Morozova, Ekaterina O [emorozov at indiana.edu];
Search NeuronDB for information about:  Cholinergic Receptors; I K; I K,Ca; I Calcium; Acetylcholine; Dopamine; Gaba; Glutamate;
function [CinppoisGlu,st]=Glu_population_fun(lambda,Tmax)
%rng('default'); rng(1);
N=Tmax;
M=50; % number of neurons
dt=Tmax/(N-1);
C00=zeros(M,N);
for j=1:M
    T(1)=0;
    i=1;
    while T(i) < Tmax
        U=rand(1,1);
        T(i+1)=T(i)-(1/lambda)*(log(U));
        i=i+1;
    end
    i=1;
    for i=2:numel(T)-1
        k=round(T(i)/Tmax*N);
        if k<=0   k=1; end
        if k>N k=N; end
        C00(j,k)=1;
    end
end
% spiketimes
for i=1:size(C00,1)
    st{i}=find(C00(i,:)>0);
end

fr=length(st{2})/Tmax

Glupoissum=sum(C00);
Glupoissum1=Glupoissum;
%Glupoissum1(Glupoissum1<2)=0;%  To simulate convergence of synaptic inputs on the DA neuron,
%we threshold NMDAR to activate only by coincidence of two or more spikes
%Glupoissum1(Glupoissum1>1)=1;
%
dt=0.02; % step of integration in mex file
CinputpoisGlu=zeros(length(Glupoissum1),1/dt);
for i=1:length(Glupoissum1)
    for j=1:1/dt
        CinputpoisGlu(i,j)= Glupoissum1(1,i);
    end
end;
CinputpoisGlu=CinputpoisGlu';
CinppoisGlu=CinputpoisGlu(:);

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