The provided code appears to simulate and visualize a model related to blocking phenomena in spatial learning, with a focus on two neural systems: the hippocampus (HPC) and dorsolateral striatum (DLS). These areas are key to spatial navigation and learning in the brain. ### Biological Context #### 1. **Hippocampus (HPC)** - The HPC is crucial for forming spatial memories and navigating environments. It utilizes spatial cues to generate cognitive maps, which help to locate goals or landmarks within an environment. - In this model, the HPC is likely represented by one of the datasets analyzed, probably reflecting the time steps or efficiency with which the model navigates or learns based on boundary cues. #### 2. **Dorsolateral Striatum (DLS)** - The DLS is involved in habit formation and procedural memory, operating in a manner often less flexible than the HPC. It can contribute to spatial learning by reinforcing repetitive actions that lead to rewards. - The data labeled by DLS in the model likely represents a different aspect of the learning curve or efficiency when cues change or become blocked. #### 3. **Blocking Phenomenon** - The blocking phenomenon in learning refers to a scenario where a new stimulus provides no additional predictive value because the outcome is already anticipated by a previously learned cue. In spatial terms, this can translate to one landmark (cue) overpowering the recognition and learning of another upon introduction. - Through visual modeling, the different stages including "Initial learning," "Compound learning," and "Testing" phases indicate the process by which cues influence behavior. The circles and scatter plots visualize these cues and outlines changes during these phases. ### Visualization and Analysis - **Landmark Cues**: The use of differently colored cues (`cue1_colour` and `cue2_colour`) likely represents distinct landmarks (L1 and L2). These are visualized in the plot with scatter symbols to depict their influence at various stages. - **Platform Representation**: The "platform" represented by circles in each subplot is likely an analogy for a goal position, such as in a water maze task where the platform could stand for a target location the subject is trained to reach. - **Results Analysis**: The graph subplot presumably depicts the learning curves for HPC and DLS regarding trials and time steps, serving as a basis for understanding the relative contributions and efficiency in spatial learning linked to these two brain regions. ### Conclusion Overall, the code provides a simulation of spatial learning affected by cue competition or blocking phenomena, highlighting the differing roles and efficiencies of the HPC and DLS in handling spatial information, goal-seeking behavior, and learning new environmental cues. The generated figures offer visual insight into how these neural systems may interact during complex learning tasks.