A basal ganglia model of aberrant learning (Ursino et al. 2018)

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Accession:239530
A comprehensive, biologically inspired neurocomputational model of action selection in the Basal Ganglia allows simulation of dopamine induced aberrant learning in Parkinsonian subjects. In particular, the model simulates the Alternate Finger Tapping motor task as an indicator of bradykinesia.
Reference:
1 . Ursino M, Baston C (2018) Aberrant learning in Parkinson's disease: A neurocomputational study on bradykinesia. Eur J Neurosci 47:1563-1582 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism: Basal ganglia;
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell;
Channel(s):
Gap Junctions:
Receptor(s): D1; D2; Cholinergic Receptors;
Gene(s):
Transmitter(s): Dopamine; Acetylcholine;
Simulation Environment: MATLAB;
Model Concept(s): Parkinson's; Synaptic Plasticity; Long-term Synaptic Plasticity;
Implementer(s): Ursino, Mauro [mauro.ursino at unibo.it]; Baston, Chiara [chiara.baston at unibo.it];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; D1; D2; Cholinergic Receptors; Acetylcholine; Dopamine;
%% program which perfroms the simulations for figure 9:  analysis of diskynesia
clear all
close all
clc
Ke = [1:1:9];
Da_min = [0.65 0.65 0.65 0.75 0.8 0.85 0.9 0.6 0.6]
Da_max = [10 10 10 10 1.6 1.55 1.5 1.15 1.55];
width = 1.5;
font = 16;
figure
plot(Da_min,Ke,'--k','linewidth',width)
hold on
plot(Da_max,Ke,'--k','linewidth',width)
axis([0 2 0.9 9.1])
xlabel('dopaminergic input','fontsize',font)
ylabel('STN strength','fontsize',font)
title('Sensitivity analysis on dyskinesia','fontsize',font)
set(gca,'fontsize',font)
text(0.3,6,'normal','fontsize',font)
text(0.95,6.5,'dyskinesia','fontsize',font)

load W_tot_new_W0e5_D1e0
    Wgc = squeeze(Wgc_epocs(:,:,100));
    Wgs = squeeze(Wgs_epocs(:,:,100));
    Wnc = squeeze(Wnc_epocs(:,:,100));
    Wns = squeeze(Wns_epocs(:,:,100));
    

 
global alpha beta gamma

% Gain from DA to Go (excitation)
alpha = 0.75;  %(0.2*(Ugo_trigger-0.8)+0.5)/(0.7*(Ugo_trigger-0.8));

%gain from DA to No-Go (inhibition)
beta = -1;

%gain form DA to the cholinergic interneuron
gamma = -0.5;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    
    
Ke = 8;   


Ns = 4;

S = zeros(Ns,1);
S(1) = 1;

STN_ON = 1;
T_ON = 1;

Dop_tonic =  1.15;
    
[Uc,C,Ugo,Go,IGo_DA_Ach,Unogo,NoGo,INoGo_DA_Ach,Ugpe,Gpe,Ugpi,Gpi,Ut,T,Ustn,STN,E,t,k_tap_vett,Uchi,ChI,ft] = BG_model_function_tapping_mauro(S,Wgc,Wgs,Wnc,Wns,Ke,STN_ON,T_ON,Dop_tonic);
Freq = ft*60
width = 1.5;
font = 18;
figure
plot(t/1000,C(1,:),'b',t/1000,C(2,:),'r','linewidth',width)
title('blue: channel 1; red = channel 2','fontsize',font)
xlabel('time (s)','fontsize',font)
ylabel('cortical neuron activity','fontsize',font)
axis([0 3 0 1.1])