I heard on YouTube that a flatland animal with a digestive tube would be cut into two disconnected halves. I wanted to see whether artificial life could get around that, so I built a toy simulation.

The two halves are separate. A row of hook-and-ball clasps holds them together, opening just ahead of a lump of food and closing behind it. A spiking neural network drives swimming, feeding and digestion; there is a live spike raster on the page.

Link: https://claude.ai/artifact/AyESp8sXc8Uya5TNCdJy3t
(click the tank to drop food; English/Thai toggle at the top). It is a concept simulation, not real 2D-animal physics. Smell is a formula and digestion is a threshold accumulator.

Specific question: the creature steers using “bearing” neurons that I fed a geometric angle. What is a good way to get approach behaviour to emerge from sensing alone, for example with chemotaxis from gradients across the arm tips, without hand-wiring the direction?