RetinaPlex: A Large-Scale Biologically Constrained Retinal Model for Parallel Visual Coding

Visual perception depends on sensory populations that represent ongoing scenes while retaining information about recent changes. How retinal diversity organizes these complementary signals for downstream computation remains incompletely understood. Here we introduce RetinaPlex, a biologically constrained retinal model comprising approximately ten million modeled retinal units and supporting 4K visual input. The model combines spatial organization constrained by human photoreceptor statistics and informed by primate retinal connectivity with physiologically inspired temporal dynamics. Across controlled stimuli and natural-video experiments, sustained and transient bipolar populations support low-capacity readout of current scene state and recent visual history from a single population snapshot, with retinal parameters held fixed. Distinct histories remain distinguishable after input sequences converge to identical continuing visual input. Model interventions show that retained-response differences shape early state–history geometry, whereas adaptation timescales modulate the relative contribution of history across pathways over time. Reanalysis of public primate retinal recordings reveals a concordant association between independently measured temporal response phenotypes and relative current-versus-recent coding bias. Comparisons with frame-like and event-like interfaces further characterize access to both information sources. Together, these findings establish RetinaPlex as a platform for investigating retinal population coding and suggest biologically grounded principles for sensory representations that jointly preserve scene state and recent change in NeuroAI and neuromorphic vision.