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]
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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: 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 GPi_neuron < handle
    %This is the class for GPi_neurons, which are inhibited by the GPe
    %neurons and excited by the STN neurons. They then inhibit neurons in
    %the PMC
    
    properties
        activity; %activity of the neuron
        t_constant %time constant
        dt; %delta t
        drgpi; %tonic drive to the GPi neuron
        wD1; %weight of the inhibitory connection between DS1 and GPi
        wstn; %weight of the excitatory connection between STN and GPi
        synaptic_input; %total synaptic input to neuron
    end
    
    methods
        %constructor
        function obj = GPi_neuron(activity,t_constant,dt,drgpi,wD1,wstn)
            obj.activity = activity;
            obj.t_constant = t_constant;
            obj.dt = dt;
            obj.drgpi = drgpi;
            obj.wD1 = wD1;
            obj.wstn = wstn;
            %obj.synaptic_input = 0;
        end
        
        function obj = update_si(obj,D1_MSN,STN_neuron)
            %Calculated synaptic input to GPi neuron
            obj.synaptic_input = obj.drgpi - (obj.wD1)*(D1_MSN.activity)...
                +(obj.wstn)*(STN_neuron.activity)+0.*rand;
        end
        
        function obj =update_activity(obj)
            %updates activity of GPi due to synaptic input
            if obj.synaptic_input <= 0
                obj.activity = obj.activity - ((obj.activity -0.1*rand)/ ...
                    obj.t_constant)*(obj.dt);
            else
                obj.activity = obj.activity + (tanh(obj.synaptic_input)...
                    +0.1*rand-obj.activity)*(1/obj.t_constant)*(obj.dt);
            end
        end
    end
end