Temporal prediction captures retinal spiking responses across animal species
Taylor, L.; Zenke, F.; King, A. J.; Harper, N. S.
Show abstract
The retinas role in visual processing has been viewed as two extremes: an efficient compressor of incoming visual stimuli akin to a camera, or as a predictor of future stimuli. Addressing this dichotomy, we developed a biologically-detailed spiking retinal model trained on natural movies under metabolic-like constraints to either encode the present or to predict future scenes. Our findings reveal that when optimized for efficient prediction[~] 100 ms into the future, the model not only captures retina-like receptive fields and their mosaic-like organizations, but also exhibits complex retinal processes such as latency coding, motion anticipation, differential tuning, and stimulus-omission responses. Notably, the predictive model also more accurately predicts the way retinal ganglion cells respond across different animal species to natural images and movies. Our findings demonstrate that the retina is not merely a compressor of visual input, but rather is fundamentally organized to provide the brain with foresight into the visual world.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Metastable attractors explain the variable timing of stable behavioral action sequences 97%
- Modulation of metastable ensemble dynamics explains the inverted-U relationship between tone discriminability and arousal in auditory cortex 96%
- Interpretable deep learning for deconvolutional analysis of neural signals 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.