The provided code is a computational model simulating the behavior of agents (which can be thought of as simplified organisms) navigating a spatial environment similar to the classic **Morris Water Maze** (MWM). This type of experiment is widely used in neuroscience to study spatial learning and memory, particularly the role of different brain regions like the hippocampus in these processes. ### Biological Basis #### Hippocampus and Striatum's Role in Spatial Navigation - **Hippocampus**: The code indicates the potential for a lesion in the hippocampus (`lesion_hippocampus = True`). In biological terms, the hippocampus is critically involved in spatial navigation and memory formation. It allows organisms to form a cognitive map of the environment, crucial for tasks like the water maze where an animal must remember the platform's location relative to spatial cues. - **Striatum**: Another structure often studied alongside the hippocampus in spatial navigation and learning is the striatum. The code provides an option for lesioning the striatum (`lesion_striatum = False`), which is not activated here. The striatum is associated with habit formation and procedural memory, providing a contrast to the hippocampal-dependent spatial learning. #### Morris Water Maze Analogy The custom environment, `HexWaterMaze`, represents a variant of the Morris Water Maze. In the biological experiment, rodents are placed in a tank filled with opaque water. They must find an escape platform using spatial cues. This code abstracts that concept into a grid-based environment with agents learning to locate platform positions through simulated episodes. #### Learning and Memory Variables - **Platform Sequence and Trials**: The platform sequence is determined randomly, simulating changes in the hidden platform location between sessions. This mimics experimental procedures where varying the platform's position tests learning flexibility and memory. - **Escape Time**: Represents how efficiently the agent (or animal, in biological terms) navigates to the platform. The reduction in escape time over sessions indicates learning, akin to how animals learn the maze over repeated trials. #### Influence of Temperature and Reinforcement Learning - **Inverse Temperature (`inv_temp`)**: In reinforcement learning models, the inverse temperature controls exploration versus exploitation behavior. Biologically, it relates to the agent's decision-making strategy, similar to how organisms might balance exploring new environments against exploiting known paths to the goal. - **Discount Factor (Gamma, `gamma`)**: Represents the importance the agent places on future rewards, related to decision-making and memory. Biologically, it reflects the emphasis on long-term planning over immediate gains. Overall, this code provides a controlled setting to simulate and study how different neural substrates contribute to learning and memory in spatial navigation, focusing primarily on the interactions and distinctions between the hippocampus and other brain regions like the striatum. The lesions simulate experimental manipulations seen in neuroscience to understand the distinct cognitive roles of these areas.