Decoding dynamic visual scenes across the brain hierarchy

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a paramount investigation within the domain of neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding dataset and utilize the capabilities of deep learning neural network models to study the question of neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. We find that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within both the visual cortex and subcortical nuclei, in contrast to a relatively diminished encoding activity within hippocampal neurons. Strikingly, our results reveal a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings not only corroborate existing knowledge in visual coding using artificial visual stimuli but illuminate the functional role of these deeper brain regions using dynamic natural scenes. Consequently, our results proffer a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding of dynamic natural visual scenes, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.