Mathematical regularities of irregular hippocampus place codes
Measurements from hippocampal place cells in small enclosures have led to a classical view of highly stereotyped neural tuning functions with smooth, unimodal tuning fields that uniformly tile the environment. However, recent experiments conducted in large spaces across multiple species have revealed a much more irregular neural code than suggested by the classical view. Indeed, place cells in large environments typically fire in multiple locations, and the multiple firing fields of individual cells, as well as those of the entire population, vary considerably in size and shape. We recently showed that a simple mathematical model, wherein firing fields are generated by thresholding realizations of a random Gaussian process, accounts for the statistical properties of place fields in precise quantitative detail. This model captures the observed statistics of field sizes and positions and generates new quantitative predictions on field shapes and topologies. Moreover, these statistics are universal across species, but sensitive to the size and dimensionality of the environment. We quantitatively verified these predictions using multiple recent datasets from bats and rodents in one, two, and three dimensions, across both small and large environments. Collectively, these findings imply that common mechanism underlie the diverse statistical features observed in different experiments and suggest that synaptic projections to CA1 are predominantly random. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-05-28. Recording duration: 00:42:45.
Locally-ordered representation of 3D space in the entorhinal cortex
When animals navigate on a two-dimensional (2D) surface, many neurons in the medial entorhinal cortex (MEC) are activated as the animal passes through multiple locations (‘firing fields’) arranged in a hexagonal lattice that tiles the locomotion-surface; these neurons are known as grid cells. However, although our world is three-dimensional (3D), the 3D volumetric representation in MEC remains unknown. Here we recorded MEC cells in freely-flying bats and found several classes of spatial neurons, including 3D border cells, 3D head-direction cells, and neurons with multiple 3D firing-fields. Many of these multifield neurons were 3D grid cells, whose neighboring fields were separated by a characteristic distance – forming a local order – but these cells lacked any global lattice arrangement of their fields. Thus, while 2D grid cells form a global lattice – characterized by both local and global order – 3D grid cells exhibited only local order, thus creating a locally ordered metric for space. We modeled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D – thus describing both 2D and 3D grid cells using one unifying model. Together, these data and model illuminate the fundamental differences and similarities between neural codes for 3D and 2D space in the mammalian brain.