Representing Part-Whole Hierarchy with Nested Neuronal Coherence

Human vision flexibly extracts part-whole hierarchies from visual scenes, but representing such structures remains a key challenge for neural networks. Inspired by the neural syntax hypothesis in neuroscience, we propose a framework for representing hierarchical part-whole relationships through nested neuronal coherence, characterized by its continuous and distributed nature. Nested neuronal coherence refers to dynamical states where distributed activations are temporally correlated to form cell assembly sequences across timescales, argued to represent neural syntax. Building on this framework, we develop a cortical-inspired hybrid model, Composer, which dynamically achieves emergent nestedness when given images. To evaluate the emergent hierarchy, we create four synthetic datasets and three quantitative metrics, demonstrating the model’s ability to parse scenes of varying complexity. Overall, our work advances a systematic paradigm spanning representation, implementation, and evaluation toward building human-like vision in neural networks.