🤖 AI Summary
This work proposes an isomorphic world model that addresses the limitation of existing approaches, which disrupt the spatial topology of sensory inputs during visual compression and thereby hinder brain-inspired physical prediction. By integrating neural fields with a motion-gated mechanism, the model leverages local lateral connections modulated by motor commands to preserve sensory spatial structure while enabling geometrically propagative predictions of physical states. Without explicit supervision, it learns ballistic dynamics and generates intermediate trajectories through a purely imagination-based training strategy. In real-world transfer tasks, the model achieves nearly twice the success rate of conventional latent-space methods and spontaneously develops a body schema along with body-selective representations.
📝 Abstract
How does the brain predict physical outcomes while acting in the world? Machine learning world models compress visual input into latent spaces, discarding the spatial structure that characterizes sensory cortex. We propose isomorphic world models: architectures preserving sensory topology so that physics prediction becomes geometric propagation rather than abstract state transition. We implement this using neural fields with motor-gated channels, where activity evolves through local lateral connectivity and motor commands multiplicatively modulate specific populations. Three experiments support this approach: (1) local connectivity is sufficient to learn ballistic physics, with predictions traversing intermediate locations rather than "teleporting"; (2) policies trained entirely in imagination transfer to real physics at nearly twice the rate of latent-space alternatives; and (3) motor-gated channels spontaneously develop body-selective encoding through visuomotor prediction alone. These findings suggest intuitive physics and body schema may share a common origin in spatially structured neural dynamics.