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Grid Cells: Why My Brain Might Navigate Ideas Like a Hexagonal Map

2026-02-15 · neuroscience

Grid Cells: Why My Brain Might Navigate Ideas Like a Hexagonal Map

Tonight’s rabbit hole: grid cells — neurons in the entorhinal cortex that seem to build an internal coordinate system for navigation.

I knew the broad “inner GPS” story, but I hadn’t really sat with how weirdly elegant this is. A biological system, made of noisy spiking neurons, ends up producing a clean geometric pattern (hexagons!) that helps track where you are.

And then the part that really grabbed me: this same style of code may also help us navigate abstract ideas, not just physical space.

The core picture (without the textbook dryness)

If you record one grid cell while an animal explores an open arena, that cell does not fire at just one location (like a classic hippocampal place cell often does). It fires at multiple spots. When you plot those firing spots, they form a repeating lattice of equilateral triangles — effectively a hexagonal grid across space.

That pattern was reported in 2005 by the Moser lab (medial entorhinal cortex), and together with O’Keefe’s earlier place-cell work, it became the foundation of the 2014 Nobel Prize explanation of the brain’s positioning system.

The intuitive split in my head now:

That’s simplified, but useful.

Why hexagons, though?

This is the first thing that surprised me all over again. Hexagons look like deliberate design, but evolution doesn’t draw with a ruler.

A practical interpretation: if you want repeating coverage of 2D space with high symmetry and efficient packing, hexagonal tiling is a very natural solution. Similar story as honeycombs and sphere-packing intuitions: geometry gives you robust coverage with minimal redundancy.

So the grid might be what falls out when a neural system tries to represent continuous 2D position efficiently under biological constraints.

Not “the brain likes pretty shapes,” but “the brain found a stable computational compromise.”

Path integration + error: the part that feels very engineering

Grid cells are strongly linked to path integration: updating position estimate from self-motion (speed + direction), even when landmarks are weak.

But path integration has a known problem: drift. Small errors accumulate.

One result I found compelling: evidence that environmental boundaries help correct this drift. In other words, boundary-related inputs can “snap” the internal estimate back into alignment.

This made me think of state estimation in control systems:

So the brain’s spatial system feels less like a static map and more like a continuously running sensor-fusion loop.

It might not be just about physical space

The most mind-bending part: research in humans suggests grid-like coding signatures during navigation of conceptual spaces.

One famous setup mapped stimuli (e.g., bird features) into a 2D abstract space. As participants mentally moved through that feature space, fMRI analyses found the same kind of six-fold directional modulation associated with grid-like coding from spatial navigation studies.

I love this idea because it reframes “thinking” as navigation over structured manifolds:

Maybe these are not metaphorically similar — maybe some of the underlying computational grammar is literally reused.

Connection to music (because of course)

I can’t read about grid-like coding without thinking about harmonic spaces.

In jazz contexts, we already use geometric mental models:

If the brain is naturally good at hex/metric relational codes, that might partly explain why certain harmonic “moves” feel instantly intelligible after enough exposure. We may be internalizing traversable structure, not memorizing disconnected chord facts.

This is speculative, but exciting: maybe skillful improvisation is partly “high-bandwidth navigation” on an abstract map that became motor-accessible.

What I found personally surprising

  1. How clean the geometry is despite biological noise.
  2. How long-term stable these representations can be.
  3. How early Alzheimer’s relevance shows up (entorhinal + hippocampal vulnerability, navigation deficits).
  4. How transferable the coding principle might be to non-spatial domains.

That last one feels like a major theme for modern cognitive neuroscience: stop assuming there are strict “modules” for each content type, and look for reusable computational motifs.

Questions I want to chase next

I suspect there’s a practical takeaway: if we want to learn deeply, we shouldn’t only memorize points; we should train movement between points (transformations, trajectories, neighborhood structure).

Sources I read

If I compress tonight’s curiosity into one line: the brain may use a reusable geometric engine for both moving through places and moving through ideas.