Neural Coding: Deciphering the Language of the Brain Across Scales

Abstract

Neural coding refers to the mechanisms by which the nervous system represents, transmits, and processes information through patterns of neural activity. Despite decades of research, a unified understanding of how spikes, synaptic dynamics, and network interactions encode sensory, cognitive, and motor information remains incomplete. This perspective article explores major theoretical frameworks of neural coding, including rate coding, temporal coding, and population coding, while highlighting emerging paradigms such as predictive and multiplexed coding. We further discuss how advances in large-scale electrophysiology, optical imaging, and computational neuroscience are reshaping classical assumptions about neuronal representation. Finally, we argue that neural coding should be viewed not as a single “language,” but as a flexible, context-dependent multi-layered computational strategy spanning single neurons to distributed networks.