effenberger_DA3_2017_script.m - Audrey Effenberger MCB 105...

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Audrey Effenberger % MCB 105: Systems Neuroscience % DA 3: Modeling reward-prediction-error learning % % TO USE THIS SCRIPT: % % Uncomment the section that corresponds to the type of % % reward-prediction-error learning you would like to simulate. % % % % PART I simulates learning with a perfect (i.e. constant) reward. % % % % PART II simulates learning with an imperfect reward. pR gives the % % probability that a reward will occur. It is currently initialized to % % 0.33 but can be changed to reflect any likelihood of reward. % % In a more complex simulation, pR could be defined as a function that % % changes with time or with some other input. % % SUMMARY: % % This program simulates a basic learning process. The variables of % % interest are the expected reward, eR, and the reward prediction error, % % rpe; their values are the normalized firing rates of dopaminergic neurons % % in the ventral tegmental area (VTA). % % % % At the beginning of the experiment, the "organism" knows nothing. The % % unconditioned stimulus (reward) has no value to the organism, so eR = 0.
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  • Fall '12
  • FlorianEngert
  • matlab, Reward, reward prediction error, legendPOS

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