Dual-feature selectivity enables bidirectional coding in visual cortical neurons

This dataset contains neural recordings and computational analyses supporting the identification of dual-feature selectivity in visual cortex. We recorded spiking activity from macaque visual areas V1 (458 neurons) and V4 (394 neurons) while animals viewed naturalistic images, as well as from mouse visual cortex areas V1 (598 neurons), LM (350 neurons), and LI (126 neurons). Using functional digital twin models—deep learning-based predictive models trained on these recordings—we systematically characterized neuronal selectivity across the full dynamic range of responses. The dataset includes: (1) 200,000 synthetically rendered scenes (236×236 pixels, PNG format) used to probe neuronal responses; (2) optimized most and least exciting inputs (MEIs/LEIs) generated through gradient-based synthesis for each neuron; (3) indices identifying the most and least activating natural images (MAIs/LAIs) from large-scale screening of ImageNet and of the Rendered Data; (4) predicted neuronal activation profiles across all stimuli; and (5) metadata including baseline firing rates, and response reliability metrics. These data reveal that many visual neurons exhibit bidirectional selectivity—responding strongly to preferred features while being systematically suppressed by distinct non-preferred features around elevated baseline firing rates. This coding strategy appears conserved across species (macaque and mouse) and visual areas (from primary to higher-order cortex), suggesting a general principle of sensory coding that balances representational capacity with interpretable single-neuron responses.