Humans use their vision in ways that other animals do not. For example, we learn to read very subtle facial expressions that can tell us whether a person is lying, angry, embarrassed, skeptical, or in a hurry. We also have an unmatched capacity to use vision to steer our hands so that we can, for example, use tools or play musical instruments. Yet despite these examples of human exceptionalism, many of our most impressive visual capacities are shared more broadly with other primates, who also use their sight for social judgements and who are also remarkably good at visually guided manual actions (e.g. in arboreal movements). More broadly, other features of our vision are used by virtually all animals, who use it to different degrees to recognize territories, build nests, avoid predators, and find mates. While these everyday problems of animals are often downplayed as innate responses, the visual problems involved are very difficult and the brains overall approach remains poorly understood. Vision is broadly used because it places an individual at a great advantage over its environment, allowing for the remote sensing of complex details from a safe distance. We recently published a comprehensive review on the evolved mechanisms of high level vision (Leopold, Freiwald, and Mitchell, Evolution of Nervous Systems, ed. Kaas, 2017) and consider this perspective essential to understanding the visual brain of humans. Much of our experimental work on visual perception centers on the brain processes shapes, objects, and scenes, starting with retinal patterns that are acted upon by specialized pathways and circuits. How does the brain create a three-dimensional internal representation of the world for perception and manual action, given that its retinal images are inherently two-dimensional? How does it integrate visual events over different time scales? How does our perception remain still despite the constant interruption by our own movements, and particularly eye movements? These types of questions are always present in the background as we study the mechanisms of visual perception. In one major project, we have recently investigated how neurons in the macaque temporal cortex respond under a paradigm that departs from the standard brief presentation of flashed stimuli. Rather than presenting stimuli for a few hundred milliseconds at a time, we allowed the subjects to view natural videos playing out over several minutes at a time. The animals were free to scan the content of the videos, as we recorded their gaze and the activity of many neurons in different regions of the high-level visual cortex. Our analysis also departed from convention in that we did not focus on the question what stimuli do the recorded neurons represent?, but rather we asked with which brain networks are the recorded neurons affiliated? For this, we combined our multiple single-unit recordings with data from functional MRI (fMRI), in which macaque subjects watched the same natural videos. Given the full-brain coverage afforded by fMRI, we were then able obtain whole-brain maps of fMRI data using single-unit activity as a sort of seed or regressor. The first paper using this method was published recently (Park et al., 2017). It emphasized the local response diversity within a local population of temporal cortex neurons. Perhaps most surprisingly, it demonstrated that neighboring neurons participated in very different whole-brain networks when analyzed through the seed correlation method mentioned above. A second manuscript on this topic, comparing local populations in multiple temporal cortex areas, is currently under preparation. In a second part of this project, we have asked the question whether the temporal structure inherent in a natural video is itself an important determinant in neural selectivity. To this end, we asked whether isolated one-second segments from a movie, when shown briefly and randomized in time, would result in similar or different neural responses to the same segments shown in context. Our initial results suggest that a local neural population is also varied in this regard, with some neurons showing minimal temporal integration and others relying strongly on the preceding video content. One key finding in this study, however, was that the initial visual transient response the product of abruptly flashing the stimulus on the screen was nearly always irrelevant to the neurons natural response pattern. This latter finding may have profound consequences for how we think about the visual brain, since most of our understanding of the cortical microcircuit, visual hierarchy, stimulus selectivity, and functional architecture are derived from experiments in which stimuli were flashed briefly onto a screen. It appears that under more continuous conditions, matching natural experience, the responses of neurons, and their similarity to their neighbors, presents a very different picture that may challenge current models. Our study of thalamocortical interactions has also had two successes in the past year. In the first, we have completed a large study about visual responses in the pulvinar, the largest thalamic nucleus in primates that is ostensibly dedicated toward vision. In that study, we measured single unit responses to a wide range of stimuli from regularly spaced intervals within the pulvinar, essentially mapping out stimulus selectivity (in a conventional sense). The results reveal some segregation of function in the pulvinar beyond the known retinotopic maps. For example, there is a concentration of neurons in and around the corticotectal tract that are notably selective for faces. In a manuscript currently under preparation, we have compared the nature of these face-selective responses with so-called face-cells in the fMRI mapped face patches of the temporal cortex. One of the principal findings is that many of the pulvinar face-selective neurons respond much earlier than the earliest responses in the face patches, suggesting that their face selectivity cannot be simply inherited from those areas. We have also been examining the responses of the same neural populations to video stimuli, and are particularly interested in the extent to which particular pulvinar neurons may contribute to the same brain networks as particular neurons in the face patches. In a second, collaborative study related to the pulvinar, we have discovered that its early disruption in marmosets affects the developmental trajectory of the dorsal visual pathway, and ultimately interferes with the normal development of visually guided grasping. Examination of adults having undergone early postnatal lesions to a specific portion of the pulvinar, abbreviated PIm, revealed morphological changes in the dorsal visual cortex in the adult, along with permanent behavioral reaching and grasping deficits compared to control animals (Mundinano et al., 2018). Thus the pulvinar is, in addition to its role in the adult, a critical structure for normal neurodevelopment.
| Ghazizadeh, Ali; Griggs, Whitney; Leopold, David A et al. (2018) Temporal-prefrontal cortical network for discrimination of valuable objects in long-term memory. Proc Natl Acad Sci U S A 115:E2135-E2144 |
| Mundinano, Inaki-Carril; Fox, Dylan M; Kwan, William C et al. (2018) Transient visual pathway critical for normal development of primate grasping behavior. Proc Natl Acad Sci U S A 115:1364-1369 |
| Tamietto, Marco; Leopold, David A (2018) Visual Cortex: The Eccentric Area Prostriata in the Human Brain. Curr Biol 28:R17-R19 |
| Dougherty, Kacie; Cox, Michele A; Ninomiya, Taihei et al. (2017) Ongoing Alpha Activity in V1 Regulates Visually Driven Spiking Responses. Cereb Cortex 27:1113-1124 |
| Takemura, Hiromasa; Pestilli, Franco; Weiner, Kevin S et al. (2017) Occipital White Matter Tracts in Human and Macaque. Cereb Cortex 27:3346-3359 |
| Taubert, Jessica; Wardle, Susan G; Flessert, Molly et al. (2017) Face Pareidolia in the Rhesus Monkey. Curr Biol 27:2505-2509.e2 |
| Park, Soo Hyun; Russ, Brian E; McMahon, David B T et al. (2017) Functional Subpopulations of Neurons in a Macaque Face Patch Revealed by Single-Unit fMRI Mapping. Neuron 95:971-981.e5 |
| Leopold, David A; Russ, Brian E (2017) Human Neurophysiology: Sampling the Perceptual World. Curr Biol 27:R71-R73 |
| Shapcott, Katharine A; Schmiedt, Joscha T; Saunders, Richard C et al. (2016) Correlated activity of cortical neurons survives extensive removal of feedforward sensory input. Sci Rep 6:34886 |
| Russ, Brian E; Kaneko, Takaaki; Saleem, Kadharbatcha S et al. (2016) Distinct fMRI Responses to Self-Induced versus Stimulus Motion during Free Viewing in the Macaque. J Neurosci 36:9580-9 |
Showing the most recent 10 out of 41 publications
