Brain-Inspired Spatial-Frequency-Aware Networks

A translation of neurophysiological findings on spatial frequency coding into artificial network design. Having found that the primate visual system processes coarse and fine spatial structure on different timescales, this project asked whether an artificial classifier benefits from the same organisation.

The resulting architecture uses two streams that separately process low and high spatial frequency content, together with a feedback mechanism inspired by the recurrent connectivity of the biological visual system. Coarse structure provides rapid context while fine detail supports precise discrimination.

The approach improves classification performance under spatial frequency filtering on CIFAR-100, indicating that biologically motivated frequency-aware processing can increase robustness when input detail is degraded.