The provided code is a simulation model of animal behavior in a spatial navigation task, specifically inspired by experiments in a water maze. Here, the focus is on modeling the role of different brain regions, particularly the hippocampus and dorsal striatum (dorsolateral striatum, DLS), in learning and memory related to spatial navigation. ### Biological Basis: #### 1. **Spatial Navigation and the Water Maze Task:** The code models an experiment reminiscent of a Morris water maze, a common task used in neuroscience to study spatial learning and memory. Animals (often rodents) are required to navigate a pool of water to find a hidden platform. This task is used to understand how different brain regions contribute to navigation and spatial memory. #### 2. **Role of the Hippocampus:** The hippocampus is critically involved in spatial navigation and the formation of spatial memories. In the code, an option (`lesion_hippocampus`) allows for simulating the effects of lesions to the hippocampus. Lesioning the hippocampus in real biological systems often results in impaired ability to remember spatial locations or form new spatial memories. This simulation therefore reflects how spatial task performance might be altered following hippocampal damage. #### 3. **Role of the Dorsal Striatum (DLS):** The dorsal striatum, involved in habit learning and procedural memory, is another region modeled in the experiment (`lesion_striatum`). Lesioning the DLS in animals can lead to difficulties in tasks that require learning from repetitive actions or developing habits. The model simulates this by alternately artificial lesioning to observe changes in behavior and learning. #### 4. **Learning Parameters:** Key parameters like the discount factor (`gamma`) and learning rate are inspired by reinforcement learning, mimicking how animals might weigh immediate versus future rewards, and how quickly they learn from mistakes or successes. The `inv_temp` parameter influences exploration-exploitation trade-offs, akin to how animals decide between trying new paths and sticking with known routes. #### 5. **Reinforcement Learning Framework:** The model uses a reinforcement learning (RL) framework represented by the `CombinedAgent`, which incorporates aspects of reward-based learning. This approach parallels how animals learn from outcomes (e.g., reaching the platform) to adjust their behavior over time, often linked to dopaminergic signaling pathways in the brain. #### 6. **Platform Travel and Sequence:** The `determine_platform_seq` function reflects variability introduced in experiments by changing platform locations over sessions, which models how animals adapt to changing environments and reassess spatial maps based on new information. ### Conclusion: Overall, this code simulates how lesions to the hippocampus and dorsal striatum can affect the learning and performance of spatial navigation tasks, reflecting the distinct contributions of these brain regions in spatial memory and habit formation. The simulation provides insights into how different neural systems interact in complex behaviors such as navigation and learning in environments.