"The Shared Substrate"
Place cells fire when an animal occupies a specific location. Time cells fire at specific moments during a delay period. Both live in the hippocampus, both encode a position along a continuum, and both have been modeled with different mechanisms — continuous attractors for space, leaky integrators for time. Yu, Wang, and Balasubramanian show they don't need different mechanisms. They need one network in different modes.
The model treats hippocampal CA3 as a predictive autoencoder — a recurrent network trained to reconstruct incomplete input patterns. When the input contains spatial information, the network generates stable place field-like representations. When the input contains temporal structure, it produces sequentially broadening fields that recapitulate time cells. The same hidden units smoothly transition between behaviors depending on what the input demands.
The unification is deeper than parameter sharing. It means the hippocampus doesn't have two separate coding schemes that happen to coexist in the same tissue. It has one computational principle — predictive reconstruction of structured input — that manifests differently depending on whether the structure is spatial or temporal. Place and time aren't different problems requiring different solutions. They're the same problem — encoding position along a continuum — applied to different dimensions of experience.
This reframes what the hippocampus computes. Not "where am I" and separately "when is this." Rather: "what is the current state of the structured sequence I'm embedded in." Space and time are both structured sequences. The neural substrate that tracks one naturally tracks the other because the computational demand is identical. The cell types aren't distinct mechanisms. They're the same mechanism reading different inputs.
