Sparse Yet Adaptive: An Interpretable Transformer for Neural Population Readout

How neural systems selectively read out reliable information from high-dimensional and redundant population activity remains a central problem in systems neuroscience. Conventional decoders can establish what information is present in neural activity, but provide limited insight into how that information may be flexibly read out. Here, we introduce a transformer-based framework that uses self-attention as an interpretable probe of population readout. Applied to large-scale macaque V1 recordings, the model revealed internal readout dynamics with clear biological correspondence: highly weighted neurons were tuned near the current stimulus, and removal of dominant readout neurons triggered recruitment of functionally similar substitutes, consistent with redundant and resilient population coding. We further applied the framework to sensory perturbations. Despite reduced contrast or moderate external noise, orientation information remained accurately recoverable, paralleling perceptual robustness. Attention analysis further suggested a possible mechanism for this stability: as sensory evidence degraded, accurate readout recruited additional neurons and weaker population interactions. Together, our framework moves neural decoding beyond black-box prediction toward an interpretable tool for uncovering computational strategies and generating mechanistic insights. Code is available at https://anonymous.4open.science/r/Transformer-model-for-accurate-orientation-coding-0751/.