Beyond Static Perception: Animals, Neurons and Synapses Move to Compute Efficiently

Neurons are often modelled as immobile systems that transmit information through chemical and electrical signals constrained by noise and bandwidth. However, this static view contrasts with biological reality, in which dynamical processes operate across scales, from whole-animal movements to motion at the subcellular level. Here, we combine recent experimental observations with biophysically realistic modelling to show that neural information processing, beyond electrochemical signalling, is dynamically shaped by motion, from morphodynamic ultrastructural changes to whole-body movements. In this framework, ultrafast mechanical adjustments in cellular and synaptic structures interact with retinal, eye, head, and body movements to accelerate encoding and enhance precision. Movement across scales dynamically alters the waveforms, latencies, refractoriness and ultrastructural organisation of quantal sampling events. These changes improve signal fidelity and extend spatiotemporal resolution, enabling neurons to generate reliable, high-speed representations with minimal delay. Through active sensing and morphodynamic sampling, animals can therefore enhance the speed and reliability of sensory representations. This perspective, in which self-generated movements across multiple scales enhance encoding and perception, offers new insight into how the brain achieves efficient, predictive and noise-resistant computation while providing a foundation for future experimental tests and biologically inspired AI designs. We first explain how motion-coupled encoding improves neural performance, focusing on edge computing in early sensory systems. We then extend these ideas more speculatively, proposing how motion-coupled sampling may have influenced the evolution of neural architectures at multiple levels, potentially contributing to efficient predictive cognition.