The code provided represents a computational model aimed at simulating synaptic plasticity processes within striatal circuits, particularly involving basal ganglia structures. This model is designed to investigate the dynamics of phasic dopamine changes and Hebbian learning mechanisms during probabilistic reversal learning tasks. Below is the biological basis relevant to this code: ### Basal Ganglia and Dopamine 1. **Striatal Circuits**: The basal ganglia, especially its striatal components, are crucial for action selection and reward-based learning. The striatum receives inputs from the cortex and is involved in processing these inputs to facilitate decision-making and motor control. 2. **Dopamine's Role**: Dopaminergic neurons from the substantia nigra project to the striatum, modulating synaptic plasticity and influencing learning processes. Dopamine operates as a neuromodulator that signals reward prediction errors—a key factor in reinforcement learning. 3. **Phasic vs. Tonic Dopamine**: The model seems to consider both phasic dopamine (transient bursts associated with rewards) and tonic dopamine levels (baseline level), where `Dop_tonic` represents tonic dopamine. ### Hebbian Learning and Synaptic Plasticity 1. **Hebbian Rules**: The model allows exploration of different Hebbian learning rules, which are methods to adjust synaptic strengths based on the activity of pre- and post-synaptic neurons. This relates to the principle that "cells that fire together, wire together." 2. **Various Hebbian Mechanisms**: The script refers to several specific synaptic plasticity rules—such as post-post, post-pre, pre-pre, and others—modeling different dynamics of neuronal activity dependency and synaptic weight updates. ### Probabilistic Reversal Learning 1. **Stimulus-Response Associations**: The model employs two stimuli with associated action choices and probabilistic rewards (`P_high` for high probability and `P_small` for low probability rewards), representing the reinforcement learning task. 2. **Reversal Learning**: This involves the ability to adapt behavior when the reward contingencies change, which relies on flexible neural adaptations within the striatal circuits, heavily modulated by dopamine signals. ### Synaptic Elements and Circuit Components 1. **Synaptic Weights**: Variables such as `Wgc`, `Wgs`, `Wnc`, and `Wns` represent synaptic connections and their adaptations over multiple learning epochs. The weights symbolize the strength of synapses between cortical areas to GO (output) or NOGO (inhibitory) pathways. 2. **Noise and Variability**: Introduction of `noise` in the stimuli reflects biological variability present in neuronal activity, which can affect synaptic plasticity and learning. 3. **GO and NOGO Pathways**: These pathways are integral to action selection, where GO facilitates movement and NOGO inhibits responses. ### Biological Insights - The model emphasizes the interaction between cortical, striatal, and dopaminergic systems in learning and adaptation. - By simulating different Hebbian learning rules, it captures the complexity of plastic changes occurring in response to reward signals, suggesting mechanisms underlying learning flexibility in the striatum. - This code reflects attempts to understand how phasic dopamine influences synaptic modifications and decision-making processes in probabilistic learning environments. In summary, this computational framework provides a nuanced examination of the cellular and synaptic actions within the basal ganglia circuits, highlighting the vital role of dopamine in modulating synaptic plasticity and learning behaviors.