The code provided is a part of a computational model aiming to replicate aspects of learning and memory in cortico-basal ganglia circuits, particularly focusing on striatal dopamine ramping. Dopamine in the basal ganglia is integral for reinforcement learning, which involves modifying behavior based on reward feedback. This model incorporates a concept of "forgetting" as it simulates the decay of synaptic values over time, which is biologically relevant for understanding the flexibility and dynamics of neuronal circuits in learning tasks.

Biological Basis

Striatal Dopamine and Reinforcement Learning

• Dopamine Ramping: Dopamine levels in the striatum often increase (ramp up) as a reward becomes imminent in reinforcement learning tasks. The ramping is thought to encode predictions of future rewards based on past experiences.
• Cortico-Basal Ganglia Circuits: These neural circuits involve connections between the cortex and basal ganglia, modulating motor planning, action selection, and learning from rewards.

Parameters and Variables

• Decay of Synaptic Weights: The model incorporates parameters (e.g., kappa1, kappa2) that simulate decay in synaptic weights, representing forgetting over trials. Biologically, this relates to synaptic plasticity, where synaptic strengths can weaken over time unless reinforced by subsequent activities.
• Reinforcement Learning Variables (p_alpha, p_gamma): These parameters resemble biological learning rates and discount factors in brain learning processes. They are critical for adjusting the extent to which new information influences the existing knowledge.
• Time and Trials: The simulations span multiple trials and time steps, mimicking the iterative nature of learning in biological systems, where repeated exposure to stimuli and feedback is essential for learning.

Computational Aspect: Simulating Biological Systems

• The code calculates how the synaptic values (Vs) evolve over each time step and trial, comparing situations with and without decay. This mirrors biological experiments where reinforcement strength changes over repeated task performance.

Biological Interpretation and Hypothesis Testing

• The model tests hypotheses about how different decay parameters (e.g., varying kappa2 values) affect the learning dynamics and the prediction errors represented by temporal difference (TD) signals. These signals are crucial in computational neuroscience for modeling the role of dopamine in reward-based learning.

In summary, this simulation represents an abstract model of how biological reinforcement learning might be modulated by changes in synaptic strength over time, contributing to a better understanding of the cortico-basal ganglia’s role in adaptive behavior and the potential role of dopamine in synaptic decay and memory.