Cortico - Basal Ganglia Loop (Mulcahy et al 2020)

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Accession:261616
The model represents learning and reversal tasks and shows performance in control, Parkinsonian and Huntington disease conditions
Reference:
1 . Mulcahy G, Atwood B, Kuznetsov A (2020) Basal ganglia role in learning rewarded actions and executing previously learned choices: Healthy and diseased states. PLoS One 15:e0228081 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism: Prefrontal cortex (PFC); Basal ganglia;
Cell Type(s): Abstract rate-based neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Rate-coding model neurons; Parkinson's; Activity Patterns; Learning; Deep brain stimulation; Oscillations; Action Selection/Decision Making; Huntington's;
Implementer(s):
classdef parkSNc_neuron < handle
    %Class for SNc, which returns reward signal
    
    properties
        e_reward; %expected reward for cue
        alpha; %constant used to update expected reward (=.15)
        signal; %reward/punishment signal from SNc
    end
    
    methods
        %constructor
        function obj = parkSNc_neuron(e_reward,alpha)
            obj.e_reward = e_reward;
            obj.alpha = alpha; 
            obj.signal = 0;
        end
        
        function obj = set_signal(obj,reward)
            %calculates signal sent from SNc due to perceived reward
            obj.signal = 0.3*(reward - obj.e_reward);
        end
        
        function obj = set_signal_ldopa(obj,reward)
            %calculate signal sent from SNc due to perceived reward, as if
            %neuron treated with L-DOPA
            obj.signal = 2*(reward - obj.e_reward);
        end
        
        function obj = update_e_reward(obj,reward)
            %Updates expected reward given current reward
            obj.e_reward = (1-obj.alpha)*(obj.e_reward)+(obj.alpha)*reward;
        end
    end
end

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