# Biological Basis of the Provided Computational Neuroscience Model The provided code is a computational model that attempts to simulate and study certain aspects of the brain's learning and decision-making mechanisms, particularly focusing on the hippocampus and dorsal striatum. Below, I describe the biological basis of the model: ## Key Brain Regions Modeled ### Hippocampus (HPC) - **Role in Learning and Memory:** The hippocampus is critical for forming new memories and has been implicated in spatial navigation and episodic memory. - **Model Representation:** The HPC in this model is used to compute state prediction errors (SPE) and is involved in calculating the successor representation (SR), a form of neural representation which captures future states an agent is expected to visit. - **Learning and Adaptation:** The model's HPC adjusts its learning rate and updates its reliability based on the SPE, paralleling how this brain region might update neural connections in response to new experiences or errors in prediction. ### Dorsal Lateral Striatum (DLS) - **Role in Habitual Behavior:** The dorsal striatum is involved in habit formation and in the procedural memory system, guiding choices based on learned value. - **Model Representation:** The DLS in this model represents model-free reinforcement learning, using a value function based on previous experience to select actions. It computes feature representations related to the environment and updates its reliability over time. - **Reliability Updates:** The DLS reliability updates capture the idea that the brain adjusts its reliance on certain neural pathways or regions based on their predictive success. ## Learning Mechanisms ### Successor Representation (SR) - **Concept:** Successor representation is a cognitive map capturing how future states depend on the current state, providing a way to predict future events based on current observations. - **Biological Relevance:** In the brain, the hippocampus and related structures likely contribute to SR, allowing rapid learning of new environments, as suggested by the model's HPC component. ### Model-free Reinforcement Learning - **Description:** This learning strategy relies on cached values from past experiences to guide decision-making, without forming a model of the environment. - **Biological Analogy:** Model-free learning, associated with the DLS, mirrors habitual and procedural learning processes, where behaviors are reinforced by repeated outcomes. ## Key Model Features - **Error Signals:** - **SPE (State Prediction Error):** The discrepancy between expected and encountered states, analogous to how animals might use prediction errors to update cognitive maps. - **RPE (Reward Prediction Error):** Difference between expected and actual rewards, a central concept to reinforcement learning theories relevant to dopamine signaling in the brain. - **Softmax Action Selection:** - Simulates decision-making processes where choices are probabilistic rather than deterministic, aligning with evidence that biological decision-making often relies on softmax-like mechanisms to balance exploration and exploitation. ## Biological Lesions - **Lesion Experiments:** The code allows simulations with lesions to the HPC or DLS, mimicking animal studies where specific areas are inactivated to study their roles in learning and decision-making. ## Experimental Context - The `DollTask` represents a decision-making task environment in a laboratory or simulated setting, capturing controlled stimulus-response scenarios akin to those used in rodent experiments to study neural correlates of decision-making. Overall, this model highlights the collaborative function of the hippocampus and dorsal striatum in learning and decision-making processes. It captures the essence of integrating prediction and error correction in biological systems, contributing insights into how complex behaviors arise from the interaction of different brain systems.