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 plots all plots (synapses and rewards) after training 
clear all
close all
clc

load W_tot_new

t = 1:1:N_epoche;

width = 1.5;
font = 18;

%plot Wgc
figure
subplot(2,2,1)
plot(t,squeeze(Wgc_epocs(1,1,1:N_epoche)),'linewidth',width)
subplot(2,2,2)
plot(t,squeeze(Wgc_epocs(1,2,1:N_epoche)),'linewidth',width)
subplot(2,2,3)
plot(t,squeeze(Wgc_epocs(2,1,1:N_epoche)),'linewidth',width)
subplot(2,2,4)
plot(t,squeeze(Wgc_epocs(2,2,1:N_epoche)),'linewidth',width)
title('W_G_C')

%plot Wgs
figure
subplot(2,2,1)
plot(t,squeeze(Wgs_epocs(1,1,1:N_epoche)),'linewidth',width)
subplot(2,2,2)
plot(t,squeeze(Wgs_epocs(1,2,1:N_epoche)),'linewidth',width)
subplot(2,2,3)
plot(t,squeeze(Wgs_epocs(2,1,1:N_epoche)),'linewidth',width)
subplot(2,2,4)
plot(t,squeeze(Wgs_epocs(2,2,1:N_epoche)),'linewidth',width)
title('W_G_S')

%plot Wnc
figure
subplot(2,2,1)
plot(t,squeeze(Wnc_epocs(1,1,1:N_epoche)),'linewidth',width)
subplot(2,2,2)
plot(t,squeeze(Wnc_epocs(1,2,1:N_epoche)),'linewidth',width)
subplot(2,2,3)
plot(t,squeeze(Wnc_epocs(2,1,1:N_epoche)),'linewidth',width)
subplot(2,2,4)
plot(t,squeeze(Wnc_epocs(2,2,1:N_epoche)),'linewidth',width)
title('W_N_C')

%plot Wns
figure
subplot(2,2,1)
plot(t,squeeze(Wns_epocs(1,1,1:N_epoche)),'linewidth',width)
subplot(2,2,2)
plot(t,squeeze(Wns_epocs(1,2,1:N_epoche)),'linewidth',width)
subplot(2,2,3)
plot(t,squeeze(Wns_epocs(2,1,1:N_epoche)),'linewidth',width)
subplot(2,2,4)
plot(t,squeeze(Wns_epocs(2,2,1:N_epoche)),'linewidth',width)
title('W_N_S')



reward_tot = sum(vett_reward)
punishment_tot = sum(vett_punishment)
no_answer_tot = sum(vett_no_risposta)


    
 % I compute the cumulative sum of all responses
 for kk = 1:N_epoche
tot_vett_no_risposta(kk) = sum(vett_no_risposta(1:kk));
tot_vett_punishment(kk) = sum(vett_punishment(1:kk));
tot_vett_reward(kk) = sum(vett_reward(1:kk));
 end
 index = (1:N_epoche);
figure
plot(index,tot_vett_no_risposta,'y',index,tot_vett_punishment,'r',index,tot_vett_reward,'g','linewidth',width)
xlabel('Number of epochs','fontsize',font)
ylabel('cumulative distribution','fontsize',font)
title('yellow: no response; red: punishment; green: reward','fontsize',font)
set(gca,'fontsize',font)