A data-driven artificial neural network model decodes dynamic visual stimuli from monkey multi-unit spiking activity, reconstructing shapes, colors, and motion with high fidelity. It spontaneously aligns with canonical cortical visual functions without region priors, and its inverse architecture predicts neural spiking from visuals, revealing reciprocal encoding-decoding in neural systems.
- Xin-Ya Zhang
- Hang Lin
- Gang Yan