Representational Similarity Analysis: Countering Incompleteness with Functional Neuronal Organization

Stimulus representations are widely used in neuroscience and machine learning to compare neural systems, such as different brain regions, organisms, and deep learning models. These representations, analyzed with decoding (perceptual) manifolds and alignment metrics such as Representational Similarity Analysis (RSA), are used for claims of similar computation. This approach has a fundamental weakness: it is misleading to assume that representational geometry is representative of a neuronal population as a whole, when such representations may be shaped by a small subset of neurons. We show that the complementary encoding paradigm addresses this: it characterizes how neurons are organized globally in terms of their responses, providing insight into how the stimulus representation is implemented by neurons within a population. We demonstrate across experiments in biological systems and deep learning models that high representational alignment can arise from small, non-representative subpopulations of neurons. Further, alignment metrics are insensitive to encoding manifold topology (how function is distributed across neurons), despite this being a key signature of differentiation across biological systems. A controlled MNIST experiment provides causal evidence: alignment metrics change only marginally even when encoding topology is causally manipulated. Overall, similarity in stimulus representations, as measured by classic alignment metrics, does not imply similarity in function or computation, motivating the use of encoding manifolds as a complementary tool for comparing neural systems.