Neural coding of natural stimuli in freely moving macaque

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Despite the fact that visual perception represents such a fundamental aspect of our everyday life, our knowledge of the underlying neural coding of natural stimuli is woefully lacking. One major limitation preventing our understanding of the neural underpinnings of natural vision is the lack of viable methodologies for recording and synchronizing eye movements and incoming visual stimuli from freely-moving monkeys during unrestrained exploratory behavior. Indeed, examining the neural bases of visual perception has been traditionally performed by studying the brain of nonhuman primates in a laboratory environment in which the head and body are restrained while synthetic stimuli are presented on a computer monitor. However, it has become increasingly clear that studying the brain in spatially confined, artificial laboratory rigs poses severe limits on our capacity to understand the function of brain circuits. To overcome these limitations, we propose a novel approach by designing an integrated wireless eye tracking system to precisely record naturally-occurring eye movements and retinotopic visual inputs throughout unstructured, freely-moving behavior in conjunction with massive, wireless electrical recordings of population activity across four cortical areas. Our novel system will be used to study the visual coding and brain state-dependence of cortical dynamics during natural visual exploration by recording population activity in multiple visual, temporal, and frontal cortical areas while nonhuman primates are interacting with their environment. We will focus on the sparseness of the population response, the real-time encoding of natural scenes, and spatial position dependency of stimulus coding. We hypothesize that the sparse coding of natural stimuli during free viewing improves the accuracy with which neural populations encode stimuli across the visual cortical hierarchy. Our proposed research will constitute a paradigm shift by moving visual neuroscience - from simply observing animal behavior and recording the responses of single cells - to a quantitative understanding of the distributed neuronal network encoding during task-free behavior in freely moving nonhuman primates interacting with their physical environment. We anticipate that the large quantity of neural data recorded using our approach will be of great interest to clinicians and computational neuroscientists studying general properties of normal and dysfunctional neural networks, possibly leading to medical insights into cortical visual impairments and innovations in cortical prosthetics for restoring natural visual functions.