While both SFA and SR provide valuable frameworks for modeling spatial representations in neuroscience, they come with distinct limitations. One potential drawback of SFA is its reliance on the assumption that the underlying features of interest change slowly over time. In rapidly changing environments or situations where the dynamics are highly non-stationary, SFA may struggle to capture relevant information, leading to suboptimal representations. Additionally, SFA typically requires a well-defined input-output mapping, which may not always be feasible in biological systems where the relationships between stimuli and neural responses are complex and nonlinear.
On the other hand, the SR, while powerful in representing state transitions and predicting future states, may not inherently capture the temporal coherence of neural signals as effectively as SFA. The SR's focus on the transition probabilities and cumulative future states can overlook the nuances of temporal dynamics that are critical in understanding neural encoding in regions such as the hippocampus. Furthermore, the SR's dependence on the underlying MDP structure may limit its applicability in scenarios where the environment is not well-defined or where the agent's actions significantly alter the state dynamics.