The Functional Role of Pinwheel Topology in the Primary Visual Cortex of High-Order Animals for Complex Natural Image Representation

Abstract

The primary visual cortex (V1) of high-level animals exhibits a complex organization of neuronal orientation preferences, characterized by pinwheel structure topology, yet the functional role of those complex patterns in natural image representation remains largely unexplored. Our study first establishes a new self-evolving spiking neural network (SESNN) model, designed to mimic the functional topological structure of orientation selectivity within V1. We observe the emergence of a particularly new "spread-out" firing patterns from center to the surround of the pinwheel structures in response to natural visual stimuli in pinwheel structures, propagating from pinwheel centers and spreading to iso-orientation domains--a pattern not found in salt- and-pepper organizations. To investigate this phenomenon, we propose a novel deep recurrent U-Net architecture to reconstruct images from V1s spiking activity across time steps and assess the encoded information entropy of different firing patterns via the models predicted uncertainty, offering a spatiotemporal analysis of V1s functional structures. Our findings reveal a trade-off between visual acuity and coding time: the "spread-out" pattern enhances the representation of complex visual details at the cost of increased response latency, while salt-and-pepper organizations, lacking such domains, prioritize rapid processing at the expense of reduced visual acuity. Additionally, we demonstrate that this trade-off is modulated by the size of iso-orientation domains, with larger domains--supported by denser neuronal populations--substantially improving both visual acuity, coding efficiency, and robustness, features diminished in smaller domains and salt-and-pepper arrangements. Our research provides a foundational understanding of the principles underlying efficient visual information representation and suggests novel strategies for advancing the robustness and performance of image recognition algorithms in artificial intelligence.