Testing Assumptions Underlying a Unified Theory for the Origin of Grid Cells

TL;DR

This study critically tests the assumptions of a unified theory explaining the origin of mammalian grid cells, using biological data, and finds evidence challenging the theory's core premises.

Contribution

It explicitly identifies key assumptions of the unified theory and empirically tests them against biological data, revealing discrepancies.

Findings

Assumptions of the unified theory are not supported by biological data.

Biological grid cells likely have a different origin than those in artificial neural networks.

Results challenge the validity of the unified theory for grid cell formation.

Abstract

Representing and reasoning about physical space is fundamental to animal survival, and the mammalian lineage expresses a wealth of specialized neural representations that encode space. Grid cells, whose discovery earned a Nobel prize, are a striking example: a grid cell is a neuron that fires if and only if the animal is spatially located at the vertices of a regular triangular lattice that tiles all explored two-dimensional environments. Significant theoretical work has gone into understanding why mammals have learned these particular representations, and recent work has proposed a ``unified theory for the computational and mechanistic origin of grid cells," claiming to answer why the mammalian lineage has learned grid cells. However, the Unified Theory makes a series of highly specific assumptions about the target readouts of grid cells - putatively place cells. In this work, we explicitly identify what these mathematical assumptions are, then test two of the critical assumptions using biological place cell data. At both the population and single-cell levels, we find evidence suggesting that neither of the assumptions are likely true in biological neural representations. These results call the Unified Theory into question, suggesting that biological grid cells likely have a different origin than those obtained in trained artificial neural networks.