Demixing Task-Conditioned Shared and Private Neural Subspaces with Temporal Attention

Single-region neural populations often mix sensory and decision-related signals, making it hard to tell which activity reflects sensory evidence, which reflects emerging choice, and when each becomes useful on a single trial. We introduce the Time-varying Subspace Autoencoder (TSA), a Poisson autoencoding model with subspace-specific temporal attention readouts. TSA decomposes time-varying neural population activity into stimulus-private, choice-private, and shared latent subspaces, and uses separate temporal attention heads to determine when each subspace contributes to stimulus and choice decoding. On synthetic data, TSA recovers the ground-truth temporal ordering and private/shared latent structure; on barrel-cortex recordings from a perceptual decision-making task, it yields an interpretable private/shared organization with single-trial temporal readout patterns in which stimulus-related signals typically precede choice-related signals, while reversed ordering is enriched on error trials. Together, these results position TSA as a tool for studying how sensory and decision signals are distributed across shared and private neural subspaces and how their readout evolves over time.