The provided code is designed to simulate and analyze computational models of learning and memory, specifically focusing on the roles of model-based and model-free strategies in tasks associated with hippocampal function in the brain. Here's the biological basis of the code: ### Biological Basis #### 1. **Model-Based and Model-Free Learning** - The code is structured to simulate the interplay between model-based and model-free learning systems. In a biological context: - **Model-Based Learning** involves using an internal model of the environment to evaluate actions based on predicted future rewards. This is thought to depend significantly on areas like the prefrontal cortex and the hippocampus. - **Model-Free Learning** relies on cached values from past experiences without considering future outcomes. This system is typically associated with structures like the basal ganglia. #### 2. **Hippocampal Function** - The main focus is on the hippocampus, a critical brain region involved in spatial navigation and memory formation. - **Two-Step Task**: This task is modeled to assess decision-making processes that require the animal, or agent, to integrate information about rewards and state transitions—functions heavily reliant on the hippocampus. - **Water Maze Task**: This is analogous to the Morris water maze, a classic experimental task used to study spatial learning and memory in rodents. Here, the task involves navigating a maze to find a platform, which is manipulated to evaluate the spatial memory capabilities of the agent with or without hippocampal involvement. #### 3. **Hippocampal Lesions and Inactivation** - The code examines the effects of lesions or partial inactivation of the hippocampus on task performance. - **Lesion Studies**: In neuroscience, lesions (either physical ablation or temporary pharmacological inactivation) are used to study the function of brain regions. Here, the simulation uses these concepts to understand how impairment of the hippocampus affects learning and decision-making processes. - **Partial Inactivation**: The parameter `inact_hpc` models variability in hippocampal function, representing scenarios where the hippocampus is not completely inactive, simulating conditions like partial damage or neurodegeneration. This approach helps in examining gradient effects on cognition and behavior. #### 4. **Parameters of Neural Activity** - Parameters like `A_alpha`, `alpha1`, `A_beta`, and `beta1` are set to explore different learning rates and influences on decision-making processes within the agents. These could correlate with various synaptic or neurotransmitter activity modulations seen during learning tasks. #### 5. **Memory and Decision-Making Analysis** - The function `get_model_weights` suggests the analysis of behavioral data to quantify how much decisions are influenced by previous outcomes and state transitions. This reflects an attempt to infer cognitive strategies (model-based vs. model-free) underlying observed behavior, analogous to real biological experimentation designed to decode decision-making circuits. ### Summary Overall, this code represents a simulation of cognitive processes in task environments that are used to study hippocampal functions and contributions to memory and decision-making. It models conditions that emulate different degrees of hippocampal impairment to understand their impacts on learning strategies, which in turn could offer insights relevant to neurological and psychiatric disorders involving memory dysfunctions, such as Alzheimer's disease.