How Advances in Brain Science Could Help Us Glue Regenerative Efforts Together

What if the metaphors we use to describe our brains are the same ones that hold back our ability to regenerate our communities, ecosystems, and economies?

Jul 09, 2025

In his latest essay, Moving Beyond the Bayesian Brain: Human–AI Collaboration and the Cognitive Langlands Bridge, LaSalle Browne does something rare: he shows how the models we use shape the limits of what we can see — and what we can build.

Why This Matters

Too often, regenerative efforts get stuck as inspiring local pilots that never add up to real system change. LaSalle Browne’s new work on brain science reminds us that the challenge isn’t just more good ideas — it’s building the “glue” that turns local fragments into resilient, coherent wholes. Whether we’re mapping neurons or neighborhoods, the lesson is the same: what holds in the overlaps determines whether we thrive in complexity or fracture under stress.

The Bayesian Brain and Its Limits

For decades, neuroscience has leaned on the “Bayesian brain” metaphor: your mind as a probability machine, constantly updating predictions based on sensory input and prior beliefs. It’s tidy and elegant — but as Madhur Mangalam’s critique shows, it’s profoundly incomplete.

LaSalle takes this further. He argues the real problem isn’t just one model — it’s our whole habit of describing living, messy, multi-scalar life with flat, linear frameworks.

Nature isn’t flat. It’s folded, recursive, and emergent. Local parts must constantly fit together into a bigger whole. When that fit breaks down, our perception breaks down too — what LaSalle calls perceptual uncertainty. Think optical illusions: your brain can’t “glue” the local signals into a clear global picture, so you see a glitch.

The Proposal: A New Bridge for Coherence

So what’s the fix? LaSalle calls for what he describes as a “Cognitive Langlands Bridge.” The original Langlands Program is a famous idea in mathematics — it’s essentially a quest for a unified theory that connects local pieces into a bigger, coherent whole. LaSalle uses this as a powerful metaphor: what if we could build the same kind of bridge in our brains — or our systems?

Think of it as a new kind of glue for complexity. Instead of relying on flat, linear models, this bridge uses advanced math (like category theory and sheaf cohomology) to test whether local signals really fit together — and then stitch them into one clear, stable picture.

It’s like how your brain checks that overlapping signals agree: when they match, you see a clear perception; when they don’t, you get illusions or blind spots.

AI comes in as a co-architect for this glue — not just to make faster guesses, but to help us spot hidden gaps or weak overlaps that humans alone might miss.

So it’s not about faster predictions for their own sake. It’s about building better mental scaffolding — new tools for gluing local fragments together — so we can navigate complexity with more coherence and fewer blind spots.

Why This Matters for Regenerative Work

Reading this, I couldn’t help but see the parallel to Regenerative Possibility Chains (RPCs) — my work aimed at helping communities turn local experiments into large-scale system resilience.

In the regenerative world, there are local pilots: a soil commons here, a new governance model there. But too often these “patches” stay disconnected. We end up with inspiring examples that don’t scale or survive stress. The glue is missing.

Gluing as a Design Principle

In cognitive science, “gluing” is another way to describe what’s called the “binding problem” — the way your brain pulls together different pieces of sensory input and holds them as a single, coherent perception.

Imagine your brain as millions of tiny local processors, each picking up fragments of sensory data: shapes, edges, colors, and sounds. These bits don’t exist in isolation — they overlap. For example, the center of your vision sees high detail while your peripheral vision picks up rough shapes.

Sheaf theory models how the brain “checks” if these overlapping pieces agree where they meet:

If they match, the brain glues them into one clear, stable perception.
If they don’t, the glue fails — you get perceptual uncertainty: an illusion, blind spot, or confusion.

So, in the brain, gluing means testing local consistency and combining local pieces into a coherent global picture.

In regenerative systems, the same principle holds:

Local pilots, experiments, or communities generate solutions.
Where they overlap — resources, governance, stories — you need structural and narrative consistency to glue them into a resilient whole.
If the overlaps don’t hold, the system fractures when pressure hits — just like an optical illusion shows you where your brain’s glue didn’t stick.

Whether it’s neurons or neighborhoods, gluing is the hidden work of coherence.

✅ Design takeaway:
Always check where your overlaps are — and whether the glue (structures, protocols, shared meaning) is strong enough to hold when stress comes.

Beyond Formal Glue: The Role of Living Semantics

But there’s more. LaSalle’s math shows how the brain’s overlaps can actually be tested: his sheaf cohomology model checks whether local neural patterns agree where they meet — if they do, you get clear perception; if they don’t, you get uncertainty or illusions.

In regenerative work, the glue is also cultural. I call this Living Semantics: the shared stories, symbols, and meaning that help communities align across differences without losing their local uniqueness.

Living Semantics is our narrative glue. It maintains coherence across contexts, allowing regenerative networks to grow and adapt without imposing uniformity.

Just like neural overlaps can be stress-tested, so can this social glue. We can listen for “narrative mismatches” — competing stories, fractured trust, misaligned incentives — that signal where the overlap might break under stress. When we see those weak spots, we can design better agreements, clearer roles, or shared language to strengthen the glue before it fails.

LaSalle reminds us that coherence can be tested, measured, and mended. In regenerative systems, we can do the same by tending to the living glue where our communities overlap.

Confluencing: Weaving the Streams Together

This ties to another innovation I’ve been exploring: R3.0’s Bioregional Confluencing. It’s the practice of intentionally weaving together people, ideas, and local experiments so they don’t just run in parallel but actually align, reinforce each other, and grow into something resilient.

LaSalle’s bridge reminds me that confluencing isn’t just feel-good community convening — it needs real structural scaffolds. Shared agreements, hinge-ready infrastructure, narrative alignment, and yes — practical ways to test whether the glue is holding.

And maybe AI has a role here too. Just as LaSalle proposes AI as a co-architect for sensing where our brain’s local signals align or fracture, regenerative communities could use AI to map where local pilots overlap or drift apart — helping us spot weak spots in the glue before they fracture under stress.

In this sense, confluencing acts like a social version of a Regenerative Langlands Bridge — a living bridge where humans and AI work together to hold coherence through complexity.

That’s where Regenerative Possibility Chains come in: RPCs are more than a map — they’re a practical tool for strengthening the glue. They help communities see how local actions connect, where overlaps need reinforcing, and how stress can be redirected toward regenerative pathways instead of fracturing the system. They make the hidden glue — overlaps, attractors, constraints, hinge-ready structures, and shared meaning — visible and actionable.

What This Inspires Next

LaSalle’s work nudges me to ask:

Where does “uncertainty” show up in our regenerative chains?
How do we test if our overlaps hold — or need repair?
Could AI become an architectural partner for mapping these patterns and helping communities better manage complexity?