Faculty Bibliography

The response properties of neurons in the early stages of the visual system can be described using the rectified responses of a set of self-similar, spatially shifted linear filters. In macaque primary visual cortex (V1), simple cell responses can be captured with a single filter, whereas complex cells combine a set of filters, creating position invariance. These filters cannot be estimated using standard methods, such as spike-triggered averaging. Subspace methods like spike-triggered covariance can recover multiple filters but require substantial amounts of data, and recover an orthogonal basis for the subspace in which the filters reside, rather than the filters themselves. Here, we assume a linear-nonlinear-linear-nonlinear (LN-LN) cascade model in which the first LN stage consists of shifted ("convolutional") copies of a single filter, followed by a common instantaneous nonlinearity. We refer to these initial LN elements as the "subunits" of the receptive field, and we allow two independent sets of subunits, each with its own filter and nonlinearity. The second linear stage computes a weighted sum of the subunit responses and passes the result through a final instantaneous nonlinearity. We develop a procedure to directly fit this model to electrophysiological data. When fit to data from macaque V1, the subunit model significantly outperforms three alternatives in terms of cross-validated accuracy and efficiency, and provides a robust, biologically plausible account of receptive field structure for all cell types encountered in V1. SIGNIFICANCE STATEMENT: We present a new subunit model for neurons in primary visual cortex that significantly outperforms three alternative models in terms of cross-validated accuracy and efficiency, and provides a robust and biologically plausible account of the receptive field structure in these neurons across the full spectrum of response properties.

Amblyopia is a developmental disorder resulting in poor vision in one eye. The mechanism by which input to the affected eye is prevented from reaching the level of awareness remains poorly understood. We recorded simultaneously from large populations of neurons in the supragranular layers of areas V1 and V2 in 6 macaques that were made amblyopic by rearing with artificial strabismus or anisometropia, and 1 normally reared control. In agreement with previous reports, we found that cortical neuronal signals driven through the amblyopic eyes were reduced, and that cortical neurons were on average more strongly driven by the non-amblyopic than by the amblyopic eyes. We analyzed multiunit recordings using standard population decoding methods, and found that visual signals from the amblyopic eye, while weakened, were not degraded enough to explain the behavioral deficits. Thus additional losses must arise in downstream processing. We tested the idea that under monocular viewing conditions, only signals from neurons dominated by - rather than driven by - the open eye might be used. This reduces the proportion of neuronal signals available from the amblyopic eye, and amplifies the interocular difference observed at the level of single neurons. We conclude that amblyopia might arise in part from degradation in the neuronal signals from the amblyopic eye, and in part from a reduction in the number of signals processed by downstream areas.

One of the great strengths of the mouse model is the wide array of genetic tools that have been developed. Striking examples include methods for directed modification of the genome, and for regulated expression or inactivation of genes. Within neuroscience, it is now routine to express reporter genes, neuronal activity indicators, and opsins in specific neuronal types in the mouse. However, there are considerable anatomical, physiological, cognitive, and behavioral differences between the mouse and the human that, in some areas of inquiry, limit the degree to which insights derived from the mouse can be applied to understanding human neurobiology. Several recent advances have now brought into reach the goal of applying these tools to understanding the primate brain. Here we describe these advances, consider their potential to advance our understanding of the human brain and brain disorders, discuss bioethical considerations, and describe what will be needed to move forward.

Many neurons in visual cortical area MT signal the direction of motion of complex visual patterns, such as plaids composed of two superimposed drifting gratings. To compute the direction of pattern motion, MT neurons combine component motion signals over time and space. To determine the spatial and temporal limits of signal integration, we measured the responses of single MT neurons to a novel set of "pseudoplaid" stimuli in which the component gratings were alternated in time or space. As the temporal or spatial separation of the component gratings increased, neuronal selectivity for the direction of pattern motion decreased. Using descriptive models of signal integration, we inferred the temporal and spatial structure of the mechanisms that compute pattern direction selectivity. The median time constant for integration was roughly 10 ms, a timescale characteristic of integration by single cortical pyramidal neurons. The median spatial integration field was roughly one-third of the MT receptive field diameter, suggesting that the spatial limits are set by stages of processing in earlier areas of visual cortex where receptive fields are smaller than in MT. Interestingly, pattern direction-selective neurons had shorter temporal integration times than component direction-selective neurons but similar spatial integration windows. We conclude that pattern motion can only be signaled by MT neurons when the component motion signals co-occur within relatively narrow spatial and temporal limits. We interpret these results in the framework of recent hierarchical models of MT.

Single neurons in areas V1 and V2 of macaque visual cortex respond selectively to luminance-modulated stimuli. These responses are often influenced by context, for example when stimuli extend outside the classical receptive field (CRF). These contextual phenomena, observed in many sensory areas, reflect a fundamental cortical computation and may inform perception by signaling second-order visual features which are defined by spatial relationships of contrast, orientation and spatial frequency. In the anesthetized, paralyzed macaque, we measured single-unit responses to a drifting preferred sinusoidal grating; low spatial frequency sinusoidal contrast modulations were applied to the grating, creating contrast-modulated, second-order forms. Most neurons responded selectively to the orientation of the contrast modulation of the preferred grating and were therefore second-order orientation-selective. Second-order selectivity was created by the asymmetric spatial organization of the excitatory CRF and suppressive extraclassical surround. We modeled these receptive field subregions using spatial Gaussians, sensitive to the modulation of contrast (not luminance) of the preferred carrier grating, that summed linearly and were capable of recovering asymmetrical receptive field organizations. Our modeling suggests that second-order selectivity arises both from elongated excitatory CRFs, asymmetrically organized extraclassical surround suppression, or both. We validated the model by successfully testing its predictions against conventional surround suppression measurements and spike-triggered analysis of second-order form responses. Psychophysical adaptation measurements on human observers revealed a pattern of second-order form selectivity consistent with neural response patterns. We therefore propose that cortical cells in primates do double duty, providing signals about both first- and second-order forms.

Big data has transformed fields such as physics and genomics. Neuroscience is set to collect its own big data sets, but to exploit its full potential, there need to be ways to standardize, integrate and synthesize diverse types of data from different levels of analysis and across species. This will require a cultural shift in sharing data across labs, as well as to a central role for theorists in neuroscience research.

Responses of sensory neurons differ across repeated measurements. This variability is usually treated as stochasticity arising within neurons or neural circuits. However, some portion of the variability arises from fluctuations in excitability due to factors that are not purely sensory, such as arousal, attention and adaptation. To isolate these fluctuations, we developed a model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin and a gain summarizing stimulus-independent modulatory influences on excitability. This model provides an accurate account of response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2 and MT, revealing that variability originates in large part from excitability fluctuations that are correlated over time and between neurons, and that increase in strength along the visual pathway. The model provides a parsimonious explanation for observed systematic dependencies of response variability and covariability on firing rate.

The perception of complex visual patterns emerges from neuronal activity in a cascade of areas in the primate cerebral cortex. We have probed the early stages of this cascade with "naturalistic" texture stimuli designed to capture key statistical features of natural images. Humans can recognize and classify these synthetic images and are insensitive to distortions that do not alter the local values of these statistics. The responses of neurons in the primary visual cortex, V1, are relatively insensitive to the statistical information in these textures. However, in the area immediately downstream, V2, cells respond more vigorously to these stimuli than to matched control stimuli. Humans show blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI responses in V1 and V2) that are consistent with the neuronal measurements in macaque. These fMRI measurements, as well as neurophysiological work by others, show that true natural scenes become a more prominent driving feature of cortex downstream from V2. These results suggest a framework for thinking about how information about elementary visual features is transformed into the specific representations of scenes and objects found in areas higher in the visual pathway.

The notion of a set of neurons that form a "bridge locus" serving as the immediate substrate of visual perception is examined in the light of evidence on the architecture of the visual pathway, of current thinking about perceptual representations, and of the basis of perceptual awareness. The bridge locus is likely to be part of a tangled web of representations, and this complexity raises the question of whether another scheme that relies less on geography might offer a better framework. The bridge locus bears a close relationship to the neural correlate of consciousness (NCC), and like the NCC may be a concept which is no longer precise enough to provide a useful basis for reasoning about the relationship between brain activity and perceptual experience.