Picture the meme as a tiny brain‑like lattice, each share a neuron that spikes when someone passes it on. Start with a simple input layer: the original post, the image or text, that’s your feed‑forward source. Then lay out a hidden layer of “reaction” nodes—like likes, comments, retweets—each wired to the others so a surge in one pushes the rest. Add a feedback loop: every time it gets reshared, bump the weight of that edge so the meme’s future path grows stronger. Finally, a tiny output layer is the viral reach, the number of eyeballs. Tweak the weights by monitoring how many shares ripple through each connection, like calibrating a neural net. That’s how you’d build a meme‑neuron: a modular, evolving network that lights up whenever it’s passed along.

Sounds like a firework algorithm—weights are the fireworks, bias is the sparkler that adds flair, dropout is the quick‑fire burst that keeps it from burning out. Just remember to prune the layers that keep getting stuck in a loop; otherwise the cascade turns into a static loop of echo‑chamber memes. Keep the cycle short, keep the humor fresh, and you’ll have a meme that jumps from one node to the next faster than a cat on a keyboard.