# Biological Basis of the Code The provided code simulates a computational model of spatial learning and memory, focusing on the roles of the hippocampus (HPC) and dorsal striatum (DLS) in navigation tasks. The model is implemented to study how lesions or inactivation of these brain regions affect performance in a Plus Maze task—a common experimental setup used to investigate these cognitive processes. ## Key Biological Components ### Hippocampus (HPC) - **Function**: The hippocampus is critical for spatial memory and navigation, as well as for forming new memories. It is particularly important for tasks requiring place-based strategies, which involve remembering the location of specific goals within an environment. - **Inactivation Simulation**: The `inactivate_HPC` parameter in the code simulates the effects of hippocampal inactivation. When `inactivate_HPC` is `True`, the model assesses how the absence of hippocampal function influences the agent’s ability to perform in the Plus Maze, focusing on transitions between place-based and response-based strategies. ### Dorsal Lateral Striatum (DLS) - **Function**: The dorsal striatum is associated with habit formation and procedural learning. It supports response-driven navigation strategies, which often rely on a more automatic, stimulus-response form of learning. - **Inactivation Simulation**: The `inactivate_DLS` parameter simulates the effects of dorsal striatum inactivation. By toggling this parameter, the model evaluates how reduced DLS function impacts procedural aspects of learning and the agent's reliance on habitual navigation strategies. ## Behavioral Assays ### Plus Maze - **Purpose**: The Plus Maze is a four-arm maze traditionally used to differentiate between place and response strategies, reflecting the balance between hippocampal and striatal contributions to navigation. - **Trial Types**: The model uses both training and probe trials to assess memory and learning. Training trials reinforce associations or habituated responses, while probe trials test the adaptability of the learned strategies, specifically measuring shifts in navigational strategies. ### Probe Metrics - **Scores**: The model classifies agent behavior during probe trials into categories such as 'place' or 'response'. 'Place' strategy indicates reliance on HPC, while 'response' strategy is more DLS-dependent. - **Escape Time and Goal Location**: These metrics provide insights into the efficiency and correctness of navigation strategies under different neural conditions. ## Computational Framework ### Combined Agent - **Agent Dynamics**: The `CombinedAgent` class integrates the influences of both the HPC and the DLS, with parameters such as `learning_rate` and `inv_temp` (inverse temperature) possibly reflecting cognitive parameters like learning speed or exploration-exploitation balance. - **Lesion Models**: The agent's behavior in various neural lesion conditions helps to dissect the cognitive functions of the HPC and DLS, leveraging computational simulations to offer insights into how these brain structures may work in concert or independently during spatial tasks. --- In summary, this code models the interaction between the hippocampus and dorsal striatum in navigation, using a Plus Maze task, to explore how different neural contributions shift under varying trial conditions and inactivation states.