Structural Plasticity as Active Inference: A Biologically-Inspired Architecture for Homeostatic Control

Researchers have developed SAPIN, a novel AI model that mimics biological brain plasticity. Unlike traditional neural networks using backpropagation, SAPIN's processing units learn by minimizing local prediction errors and can physically migrate across a 2D grid to optimize their positions. Tested on the CartPole reinforcement learning benchmark, the model achieved an 82% success rate over 100 episodes after its parameters were locked, demonstrating robust performance through homeostatic control.