The provided code models phasic dopamine changes and Hebbian plasticity mechanisms within striatal circuits during probabilistic reversal learning. This computational study mimics some key biological processes involved in decision-making and learning observed in the basal ganglia, an area of the brain involved in action selection and reward-based learning. ### Biological Basis #### **Striatal Circuits and the Basal Ganglia Model** - **Striatum Function**: The striatum, part of the basal ganglia, plays a critical role in habit formation and the learning of action-reward associations. The code simulates how synaptic strengths within this circuitry change in response to learning tasks. - **Dopaminergic Modulation**: Dopamine is a neurotransmitter crucial for reward processing and plasticity in the basal ganglia. The code implements phasic (burst-like) dopamine changes, which reflect surprise or prediction errors related to the rewards. - **Cholinergic Interactions**: The term "Ach" implies the presence of acetylcholine, another neurotransmitter that interacts with dopamine to modulate learning in the striatum. Dopamine and acetylcholine levels influence whether synapses strengthen or weaken, critical for learning. - **Noise Implementation**: Real neurons exhibit random fluctuations in membrane potentials (noise), impacting their activity. The code includes noise in the inputs to simulate this biological realism. #### **Probabilistic Reversal Learning** - **Stimuli and Responses**: The model involves two stimuli (S1 and S2) with distinct reward probabilities. This setup aims to replicate scenarios where organisms must learn which actions lead to higher rewards in uncertain conditions. - **Reversal Mechanism**: After a set of epochs, reward probabilities associated with actions and stimuli are reversed. This challenges the model to adapt, mimicking how animals can learn to change previously acquired associations when reward outcomes change. #### **Hebbian Plasticity** - **Learning Rules**: The model incorporates multiple Hebbian-like learning rules (post-pre, post-post, pre-pre), which conceptualize "cells that fire together, wire together". This form of synaptic plasticity is believed to underlie learning and memory in the brain. - **Synaptic Weight Updates**: During epochs, synaptic weights are adjusted based on selected rules, reflecting the strengthening or weakening of synapses as seen in biological systems due to activity patterns. #### **Reward and Choice Processing** - **Reward and Punishment**: The model reflects the biological concepts of reward and punishment by tracking successful actions and errors, reinforcing actions leading to rewards while discouraging others. - **Action Selection**: The basal ganglia are pivotal for deciding between competing actions. This functionality is reflected by the competition between choices, represented by "action" and changes in synaptic strengths that decide the "winning" action. ### Summary This computational code models how the basal ganglia might support learning through the interaction of dopamine, acetylcholine, and Hebbian synaptic plasticity. It simulates the neural basis of probabilistic decision-making and adapts to changing reward structures, capturing the essence of how organisms use learning to optimize behaviors based on predicted outcomes in an uncertain environment.