Start here

Welcome. There's one short way in and three longer ones. First, a warning, so nothing catches you off guard: Every idea and pattern is a draft that's open for review. The Manifesto is the only piece that's been published in a final version. The paths below follow the order in which the ideas build on each other, so you can read them as a course or dip in anywhere.

Ten minutes, one page

If you have ten minutes, spend most of them on one page.

Adaptation: how a cell learns to ignore a steady signal. It's the one finished concept page, and it shows a single cell resetting its own receptors so it can keep noticing change.
Adaptation loop. A short pattern page that retells the same loop as a part of the cell's machinery, which is a good way to check that the main idea stuck.
History-dependence versus memory. Adaptation ends on a loose end - does a methylated receptor count as memory? - and this page is where that question lives.

The cell, from the bottom up

This path builds up what a bacterium is and how it finds its way, from the first question to the full loop of sensing and moving.

Starting from cells to explain minds. It sets out why we begin with single cells at all, which is the first thing anyone should ask us.
The cell as the unit of explanation. Once you start from cells, this page says why the whole living cell is the thing to explain.
Scale, diffusion, and life at low Reynolds number. Physics at the size of a bacterium is strange to creatures our size, and everything later depends on it.
Noise and limits of molecular sensing. A cell senses by counting molecules that turn up at random, so this comes right after the physics of diffusion.
Nonequilibrium maintenance and energetic cost. Staying alive costs energy, and so does sensing, and this page works out the budget.
Boundary, metabolism, and self-production. With energy in view, this page shows how a cell keeps making the membrane that holds it together.
Receptors and signal transduction. Now the cell has a boundary and a noise problem, so we can look at the receptors that sit in that boundary.
Molecular motors and cellular action. Sensing is half the story; this page covers the motor that lets a cell act.
Run-and-tumble navigation. Put receptors and motors together and you get the run-and-tumble swimming of E. coli.
Temporal comparison. A swimming cell compares now with a few seconds ago instead of measuring across its own body, and this page explains why.
Adaptation and integral feedback. Here the comparison gets its mechanism, and this is the page that's written in full.
Information, gain, and dynamic range. With adaptation in hand, you can ask how much a signaling pathway can tell apart.
Sensorimotor behavior in a changing environment. The path closes the loop: sensing changes movement, and movement changes what gets sensed next.

The mind, carefully

This path picks up where the cell path ends and asks which cognitive words, if any, fit what cells do. It assumes you've walked the cell path, or at least read the Adaptation page.

Levels and kinds of explanation. Before you reach for words like memory, it helps to separate observation, mechanism, function and history.
Grading claims by evidence. This page builds the habit of asking what kind of support each claim has, and you'll need that habit from here on.
History-dependence versus memory. The first careful distinction: a cell can be shaped by its past without storing it in a testable way.
Cellular memory substrates and timescales. Once memory has a definition, this page asks what physical states could carry it.
Habituation without neurons. A first test case of learning in single cells, with the controls it needs.
Associative learning and its controls. A harder claim than habituation, so it comes next.
Prediction and adaptive anticipation. This builds on learning and asks whether cells get ready for what a cue usually predicts.
Viability and organism-relative normativity. Here we go back to the cell's own stakes, because the debates ahead lean on the idea that things can go better or worse for a cell.
Competing criteria for minimal cognition. With memory, learning and stakes in view, you can compare where different authors draw the line for cognition.
Adaptivity, valence, and natural agency. This page adds agency and valence to the criteria from the last step.
Representation, computation, and dynamical alternatives. Two rival ways of describing what a cell does, and why the choice matters.
Cognition, subjectivity, sentience, and consciousness. The path ends at the biggest words. We keep them apart from each other so each can be judged on its own evidence.

Design, or inventing cells

This path is for people who want to invent cells and experiments. It assumes the cell path, and the mind path helps with the last few steps.

Levels and kinds of explanation. Design work starts from knowing which kind of claim a new idea is making.
Grading claims by evidence. A designer has to keep what's known apart from what's proposed.
Actual and possible: the modal status of a claim. This sharpens the last page by asking whether a behavior is real or only possible.
Counterfactual biological imagination. With that care in place, you can start inventing alternatives to familiar cellular solutions.
Viability and organism-relative normativity. An invented organism needs stakes, and this page says what they are for a cell.
The cell-relative environment. It also needs a world it can detect and act on at its own scale.
Designing organisms and their environments together. Now organism and world can be specified as a pair.
Constraints, tradeoffs, and realizability. Every design then gets checked against the physical limits from the cell path.
Inventing observables and experimental worlds. A design is only useful if someone could measure what it claims, and that's what this page is about.
Comparing alternative implementations. Here you model two mechanisms for one task, with adaptation as a worked case.
Speculative natural history and discovery. This page describes an imagined organism in full and asks how anyone would ever find it.
Whole-cell modeling. Larger designs need models of the whole cell, and this page leans on the memory and variability pages of the mind strand.
Synthetic tests of whether a retained state matters for viability. How to engineer a cell that keeps a record, and then test whether the record matters.
Rival models and discriminating interventions. With models and tests in hand, you can design experiments that tell rival explanations apart.
Iterative whole-cell design and research proposal. The last page gathers everything into one design and a proposal to test it. That's a lot to ask of one path, and we know it.

Each concept page lists its own prerequisites near the top, so if a page leans on something you skipped, you can step back to it.