Spatiotemporal Abstraction Theory: Re‐Interpretation of Localized Cortical Networks

Abstract The brain excels at extracting meaning from noisy and degraded input, yet the computational principles that underlie this robustness remain unclear. We propose a theory of spatiotemporal abstraction (STA), in which localized cortical networks integrate inputs across space and time to produce multi-scale, concept-level representations that remain stable despite loss of detail. We demonstrate how this principle explains a long-standing paradox of how cochlear implant patients can understand speech despite severely scrambled neural patterns. STA provides a unified framework that explains fundamental questions: Why do we have so many neurons that respond very similarly in one cortical location? Why do we have different inhibitory neurons? It also forces us to re-examine long-standing explanations of memory, creativity, illusions, attractor dynamics, excitatory-to-inhibitory balance, and the structure and purpose of the ubiquitous canonical circuits seen throughout the brain. We conclude with STA implications for improving neural implants and artificial neural networks. Competing Interest Statement The authors have declared no competing interest. Footnotes gfridma1{at}jhmi.edu