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The retinal image is insufficient for determining what is “out there,” because many different real-world geometries could produce any given retinal image. Thus, the visual system must infer which external cause is most likely, given both the sensory data and prior knowledge that is either innate or learned via interactions with the environment. We will describe a general framework of “hierarchical Bayesian inference” that we and others have used to explore the role of cortico-cortical feedback in the visual system, and we will further argue that this approach to “seeing” makes our visual systems prone to perceptual errors in a variety of different ways. In this deliberately provocative and biased perspective, we argue that the neuromodulator, dopamine, may be a crucial link between neural circuits performing Bayesian inference and the perceptual idiosyncrasies of people with schizophrenia.

Hereditary hearing loss often results from mutation of genes expressed by cochlear hair cells. Gene addition using AAV vectors has shown some efficacy in mouse models, but clinical application requires two additional advances. First, new AAV capsids must mediate efficient transgene expression in both inner and outer hair cells of the cochlea. Second, to have the best chance of clinical translation, these new vectors must also transduce hair cells in non-human primates. Here, we show that an AAV9 capsid variant, PHP.B, produces efficient transgene expression of a GFP reporter in both inner and outer hair cells of neonatal mice. We show also that AAV9-PHP.B mediates almost complete transduction of inner and outer HCs in a non-human primate. In a mouse model of Usher syndrome type 3A deafness (gene CLRN1), we use AAV9-PHP.B encoding Clrn1 to partially rescue hearing. Thus, we have identified a vector with promise for clinical treatment of hereditary hearing disorders, and we demonstrate, for the first time, viral transduction of the inner ear of a primate with an AAV vector. Keywords: inner ear, AAV, non-human primate, cochlea, hair cells, adeno-associated virus vector, hereditary deafness, gene delivery

The local field potential (LFP) is generally thought to be dominated by synaptic activity within a few hundred microns of the recording electrode. The sudden onset of a visual stimulus causes a large downward deflection of the LFP recorded in primary visual cortex, known as a visually evoked potential (VEP), followed by rhythmic oscillations in the gamma range (30-80 Hz) that are often in phase with action potentials of nearby neurons. By inactivating higher visual areas that send feedback projections to V1, we produced a large decrease in amplitude of the VEP, and a strong attenuation of gamma rhythms in both the LFP and multi-unit activity, despite an overall increase in neuronal spike rates. Our results argue that much of the recurrent, rhythmic activity measured in V1 is strongly gated by feed-back from higher areas, consistent with models of coincidence detection that result in burst firing by layer 5 pyramidal neurons.

Most approaches to visual prostheses have focused on the retina, and for good reasons. The earlier that one introduces signals into the visual system, the more one can take advantage of its prodigious computational abilities. For methods that make use of microelectrodes to introduce electrical signals, however, the limited density and volume occupying nature of the electrodes place severe limits on the image resolution that can be provided to the brain. In this regard, non-retinal areas in general, and the primary visual cortex in particular, possess one large advantage: “magnification factor” (MF)—a value that represents the distance across a sheet of neurons that represents a given angle of the visual field. In the foveal representation of primate primary visual cortex, the MF is enormous—on the order of 15–20 mm/deg in monkeys and humans, whereas on the retina, the MF is limited by the optical design of the eye to around 0.3 mm/deg. This means that, for an electrode array of a given density, a much higher- resolution image can be introduced into V1 than onto the retina (or any other visual structure). In addition to this tremendous advantage in resolution, visual cortex is plastic at many different levels ranging from a very local ability to learn to better detect electrical stimulation to higher levels of learning that permit human observers to adapt to radical changes to their visual inputs. We argue that the combination of the large magnification factor and the impressive ability of the cerebral cortex to learn to recognize arbitrary patterns, might outweigh the disadvantages of bypassing earlier processing stages and makes V1 a viable option for the restoration of vision.

The response of a sensory neuron to an unchanging stimulus typically adapts, showing decreases in response gain that are accompanied by changes in the shape of tuning curves. It remains unclear whether these changes arise purely due to spike rate adaptation within single neurons or whether they are dependent on network interactions between neurons. Further, it is unclear how the timescales of neural and perceptual adaptation are related. To examine this issue, we compared speed tuning of middle temporal (MT) and medial superior temporal neurons in macaque visual cortex after adaptation to two different reference speeds. For 75% of speed-tuned units, adaptation caused significant changes in tuning that could be explained equally well as lateral shifts, vertical gain changes, or both. These tuning changes occurred rapidly, as both neuronal firing rate and Fano factor showed no evidence of changing beyond the first 500 ms after motion onset, and the magnitude of tuning curve changes showed no difference between trials with adaptation durations shorter or longer than 1 s. Importantly, the magnitude of tuning shifts was correlated with the transient-sustained index, which measures a well characterized form of rapid response adaptation in MT, and is likely associated with changes at the level of neuronal networks. Tuning curves changed in a manner that increased neuronal sensitivity around the adapting speed, consistent with improvements in human and macaque psychophysical performance that we observed over the first several hundred ms of adaptation.

The past decade has seen a dramatic increase in our knowledge of the neural basis of stereopsis. New cortical areas have been found to represent binocular disparities, new representations of disparity information (e.g., relative disparity signals) have been uncovered, the first topographic maps of disparity have been measured, and the first causal links between neural activity and depth perception have been established. Equally exciting is the finding that training and experience affects how signals are channeled through different brain areas, a flexibility that may be crucial for learning, plasticity, and recovery of function. The collective efforts of several laboratories have established stereo vision as one of the most productive model systems for elucidating the neural basis of perception. Much remains to be learned about how the disparity signals that are initially encoded in primary visual cortex are routed to and processed by extrastriate areas to mediate the diverse capacities of three-dimensional vision that enhance our daily experience of the world.

Hierarchical organization is a common feature of mammalian neocortex. Neurons that send their axons from lower to higher areas of the hierarchy are referred to as "feedforward" (FF) neurons, whereas those projecting in the opposite direction are called "feedback" (FB) neurons. Anatomical, functional, and theoretical studies suggest that these different classes of projections play fundamentally different roles in perception. In primates, laminar differences in projection patterns often distinguish the two projection streams. In rodents, however, these differences are less clear, despite an established hierarchy of visual areas. Thus the rodent provides a strong test of the hypothesis that FF and FB neurons form distinct populations. We tested this hypothesis by injecting retrograde tracers into two different hierarchical levels of mouse visual cortex (area 17 and anterolateral area [AL]) and then determining the relative proportions of double-labeled FF and FB neurons in an area intermediate to them (lateromedial area [LM]). Despite finding singly labeled neurons densely intermingled with no laminar segregation, we found few double-labeled neurons (≈5% of each singly labeled population). We also examined the development of FF and FB connections. FF connections were present at the earliest timepoint we examined (postnatal day 2, P2), while FB connections were not detectable until P11. Our findings indicate that, even in cortices without laminar segregation of FF and FB neurons, the two projection systems are largely distinct at the neuronal level and also differ with respect to the timing of their axonal outgrowth.

The primate visual cortex exhibits a remarkable degree of interconnectivity. Each visual area receives an average of 10 to 15 inputs, many of them from cortical areas with overlapping, but not identical, functional properties. In this study, we assessed the functional significance of this anatomical parallelism to the middle temporal area (MT) of the macaque visual cortex. MT receives major feedforward inputs from areas V1, V2, and V3, but little is known about the properties of each of these pathways. We previously demonstrated that reversible inactivation of V2 and V3 causes a disproportionate degradation of tuning for binocular disparity of MT neurons, relative to direction tuning (Ponce et al., 2008). Here we show that MT neurons continued to encode speed and size information during V2/3 inactivation; however, many became significantly less responsive to fast speeds and others showed a modest decrease in surround suppression. These changes resemble previously reported effects of reducing stimulus contrast (Pack et al., 2005; Krekelberg et al., 2006), but we show here that they differ in their temporal dynamics. We find no evidence that the indirect pathways selectively target different functional regions within MT. Overall, our findings suggest that the indirect pathways to MT primarily convey modality-specific information on binocular disparity, but that they also contribute to the processing of stimuli moving at fast speeds.

Surround suppression contributes to important functions in visual processing, such as figure-ground segregation; however, this benefit comes at the cost of decreased neuronal sensitivity. Studies of receptive fields at several levels of the visual hierarchy have demonstrated that surround suppression is reduced for low contrast stimuli, thereby improving neuronal sensitivity. We investigated whether this reduction of surround suppression reflects a general processing strategy to boost sensitivity for weak signals by summing them over a larger region of the visual field (spatial integration) or if the reduction is limited to specialized stimulus conditions. To do this, we used stochastic motion stimuli to measure surround suppression in area MT of alert macaque monkeys. While varying stimulus size we also varied the strength of two other critical stimulus features: contrast and coherence (i.e., the proportion of dots moving in the preferred direction of the neuron). We found that reducing stimulus contrast weakened surround suppression, but reducing stimulus coherence had the opposite effect, indicating that diminished surround suppression is not a universal response to stimuli of low signal-to-noise. This can be partially explained by our other finding, which is that surrounds in MT are very broadly direction tuned. Instead of producing a reduction of surround suppression that would improve the ability of the neuron to integrate preferred direction motion, low coherence stimuli activated the broadly tuned surrounds relatively better than the centers, which are generally more direction selective. Our results are consistent with a normalization mechanism of surround suppression that pools broadly across multiple stimulus dimensions.

The contribution of sensory neurons to perceptual decisions about external stimulus events has received much attention, but it is less clear how sensory responses are integrated over time to produce decisions that are both rapid and reliable. To address this issue, we recorded from middle temporal area and medial superior temporal area neurons in rhesus macaques performing a task requiring the detection and discrimination of unpredictable speed changes. We examined how neuronal activity encoded the sign of the speed change and predicted the animals' behavioral judgments and reaction times, with a focus on the timescales over which neuronal activity is informative. False detection trials, on which animals reported a speed change even though none had occurred, were grouped according to the animals' discrimination judgment. By comparing the neuronal responses between the two groups of false detection trials, we were able to predict the animals' choices from the sensory activity of single neurons at levels significantly better than chance. These choice probability measurements were strongest using spike counts in an 80 ms window ending 150 ms before a choice saccade began, but significant choice probabilities were observed in windows as short as 10 ms. While the maximum deviation in spiking rate following a speed change is evident in the transient response, averaging neuronal activity in longer time windows can be more informative about both the stimulus and the animals' behavioral judgments. Thus the timescales found in this study represent a trade-off between producing rapid reactions and overcoming the noise inherent in short time windows.

Neurons in the primate extrastriate cortex are highly selective for complex stimulus features such as faces, objects, and motion patterns. One explanation for this selectivity is that neurons in these areas carry out sophisticated computations on the outputs of lower-level areas such as primary visual cortex (V1), where neuronal selectivity is often modeled in terms of linear spatiotemporal filters. However, it has long been known that such simple V1 models are incomplete because they fail to capture important nonlinearities that can substantially alter neuronal selectivity for specific stimulus features. Thus a key step in understanding the function of higher cortical areas is the development of realistic models of their V1 inputs. We have addressed this issue by constructing a computational model of the V1 neurons that provide the strongest input to extrastriate cortical middle temporal (MT) area. We find that a modest elaboration to the standard model of V1 direction selectivity generates model neurons with strong end-stopping, a property that is also found in the V1 layers that provide input to MT. With this computational feature in place, the seemingly complex properties of MT neurons can be simulated by assuming that they perform a simple nonlinear summation of their inputs. The resulting model, which has a very small number of free parameters, can simulate many of the diverse properties of MT neurons. In particular, we simulate the invariance of MT tuning curves to the orientation and length of tilted bar stimuli, as well as the accompanying temporal dynamics. We also show how this property relates to the continuum from component to pattern selectivity observed when MT neurons are tested with plaids. Finally, we confirm several key predictions of the model by recording from MT neurons in the alert macaque monkey. Overall our results demonstrate that many of the seemingly complex computations carried out by high-level cortical neurons can in principle be understood by examining the properties of their inputs.

Neurons in the cerebral cortex respond inconsistently to a repeated sensory stimulus, yet they underlie our stable sensory experiences. Although the nature of this variability is unknown, its ubiquity has encouraged the general view that each cell produces random spike patterns that noisily represent its response rate. In contrast, here we show that reversibly inactivating distant sources of either bottom-up or top-down input to cortical visual areas in the alert primate reduces both the spike train irregularity and the trial-to-trial variability of single neurons. A simple model in which a fraction of the pre-synaptic input is silenced can reproduce this reduction in variability, provided that there exist temporal correlations primarily within, but not between, excitatory and inhibitory input pools. A large component of the variability of cortical neurons may therefore arise from synchronous input produced by signals arriving from multiple sources.

Current limitations in technology have prevented an extensive analysis of the connections among neurons, particularly within nonmammalian organisms. We developed a transsynaptic viral tracer originally for use in mice, and then tested its utility in a broader range of organisms. By engineering the vesicular stomatitis virus (VSV) to encode a fluorophore and either the rabies virus glycoprotein (RABV‐G) or its own glycoprotein (VSV‐G), we created viruses that can transsynaptically label neuronal circuits in either the retrograde or anterograde direction, respectively. The vectors were investigated for their utility as polysynaptic tracers of chicken and zebrafish visual pathways. They showed patterns of connectivity consistent with previously characterized visual system connections, and revealed several potentially novel connections. Further, these vectors were shown to infect neurons in several other vertebrates, including Old and New World monkeys, seahorses, axolotls, and Xenopus. They were also shown to infect two invertebrates, Drosophila melanogaster, and the box jellyfish, Tripedalia cystophora, a species previously intractable for gene transfer, although no clear evidence of transsynaptic spread was observed in these species. These vectors provide a starting point for transsynaptic tracing in most vertebrates, and are also excellent candidates for gene transfer in organisms that have been refractory to other methods. J. Comp. Neurol. 523:1639–1663, 2015. © 2015 Wiley Periodicals, Inc.

SummaryThe activity of individual sensory neurons can be predictive of an animal’s choices. These decision signals arise from network properties dependent on feedforward and feedback inputs; however, the relative contributions of these inputs are poorly understood. We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks. During each session, we reversibly inactivated V2 and V3, which provide feedforward input to MT that conveys more information about depth than motion. We thus monitored the choice-related activity of the same neuron both before and during V2/V3 inactivation. During inactivation, MT neurons became less predictive of decisions for the depth task but not the motion task, indicating that a feedforward pathway that gives rise to tuning preferences also contributes to decision signals. We show that our data are consistent with V2/V3 input conferring structured noise correlations onto the MT population.

The nervous system is complex not simply because of the enormous number of neurons it contains but by virtue of the specificity with which they are connected. Unraveling this specificity is the task of neuroanatomy. In this endeavor, neuroanatomists have traditionally exploited an impressive array of tools ranging from the Golgi method to electron microscopy. An ideal method for studying anatomy would label neurons that are interconnected, and, in addition, allow expression of foreign genes in these neurons. Fortuitously, nature has already partially developed such a method in the form of neurotropic viruses, which have evolved to deliver their genetic material between synaptically connected neurons while largely eluding glia and the immune system. While these characteristics make some of these viruses a threat to human health, simple modifications allow them to be used in controlled experimental settings, thus enabling neuroanatomists to trace multi-synaptic connections within and across brain regions. Wild-type neurotropic viruses, such as rabies and alpha-herpes virus, have already contributed greatly to our understanding of brain connectivity, and modern molecular techniques have enabled the construction of recombinant forms of these and other viruses. These newly engineered reagents are particularly useful, as they can target genetically defined populations of neurons, spread only one synapse to either inputs or outputs, and carry instructions by which the targeted neurons can be made to express exogenous proteins, such as calcium sensors or light-sensitive ion channels, that can be used to study neuronal function. In this review, we address these uniquely powerful features of the viruses already in the neuroanatomist’s toolbox, as well as the aspects of their biology that currently limit their utility. Based on the latter, we consider strategies for improving viral tracing methods by reducing toxicity, improving control of transsynaptic spread, and extending the range of species that can be studied.

In primary visual cortex (V1), neuronal responses are sensitive to context. For example, responses to stimuli presented within the receptive field (RF) center are often suppressed by stimuli within the RF surround, and this suppression tends to be strongest when the center and surround stimuli match. We sought to identify the mechanism that gives rise to these properties of surround modulation. To do so, we exploited the stability of implanted multielectrode arrays to record from neurons in V1 of alert monkeys with multiple stimulus sets that more exhaustively probed center-surround interactions. We first replicated previous results concerning center-surround similarity using gratings representing all combinations of center and surround orientation. With this stimulus set, the surround simply scaled population responses to the center, such that the overall population tuning curve had the same shape and peak response. However, when the center contained two superimposed gratings (i.e., a visual “plaid”), one component of which always matched the surround orientation, suppression selectively affected the portion of the response driven by the matching center component, thereby producing shifts in the peak of the population orientation tuning curve. In effect, the surround caused neurons to respond predominantly to the component grating of the center plaid that was unmatched to the surround grating, as if by reducing the effective strength of whichever stimulus attributes were matched to the surround. These results provide key physiological support for theoretical models that propose feature-specific, input-gain control as the mechanism underlying surround suppression.

The primate visual system is arranged hierarchically, starting from the retina and continuing through a series of extrastriate visual areas. Selectivity for motion is first found in individual neurons in the primate visual cortex (V1), in which many simple cells respond selectively to the direction and speed of moving stimuli. Beyond simple cells, most studies of direction selectivity have focused on either V1 complex cells or neurons in the middle temporal area (MT/V5). To understand how visual information is transferred along this pathway, we have studied all three types of neurons, using a reverse correlation procedure to obtain high spatial and temporal resolution maps of activity for different motion stimuli. Most complex and MT cells showed strong second-order interactions, indicating that they were tuned for particular displacements of an apparent motion stimulus. The spatiotemporal structure of these interactions showed a high degree of similarity between the populations of V1 complex cells and MT cells, in terms of the spatiotemporal limits and preferences for motion and their two-dimensional spatial structure. Much of the structure in the V1 and MT second-order kernels could be accounted for on the basis of the first-order responses of V1 simple cells, under the assumption of a Reichardt or motion-energy type of computation.

The smooth pursuit system must integrate many local motion measurements into a coherent estimate of target velocity. Several laboratories have studied this integration process using eye movements elicited by targets, such as tilted bars, containing conflicts between local motion signals measured along contours [one dimensional (1D)] and those measured at the bar's endpoints, or terminators [two dimensional (2D)]. The general finding is that 1D signals dominate early responses, whereas later components of the behavior are determined by 2D signals. We studied the dynamics of the integration process in macaque monkeys by systematically varying the relative proportions of 1D and 2D signals and the retinal eccentricities at which they appeared. Predictably, longer bars produced greater and longer-lasting contour-induced deviations. The evolution of the 2D response occurred over a period of 50-400 ms, depending on the relative proportions of 1D and 2D signals. As contours were displaced from the fovea the deviation decreased but much less so for early (1st 40 ms) than for late (subsequent 40 ms) pursuit initiation. These bottom-up effects could be overcome to a limited extent by the top-down influence of predictability. Finally, we observed that when animals were free to track any part of the bar, they spontaneously made short-latency saccades to the terminators on most trials, especially when the bars were tilted. This suggests an increased saliency of moving terminators, particularly when discrepancies exist among local motion signals.

The integration of visual information is a critical task that is performed by neurons in the extrastriate cortex of the primate brain. For motion signals, integration is complicated by the geometry of the visual world, which renders some velocity measurements ambiguous and others incorrect. The ambiguity arises because neurons in the early stages of visual processing have small receptive fields, which can only recover the component of motion perpendicular to the orientation of a contour (the aperture problem). Unambiguous motion signals are located at end points and corners, which are referred to as terminators. However, when an object moves behind an occluding surface, motion measurements made at the terminators formed by the intersection of the object and the occluder are generally not consistent with the direction of object motion. To study how cortical neurons integrate these different motion cues, we used variations on the classic “barber pole” stimulus and measured the responses of neurons in the middle temporal area (MT or V5) of extrastriate cortex of alert macaque monkeys. Our results show that MT neurons are more strongly influenced by the unambiguous motion signals generated by terminators than to the ambiguous signals generated by contours. Furthermore, these neurons respond better to terminators that are intrinsic to a moving object than to those that are accidents of occlusion. V1 neurons show similar response patterns to local cues (contours and terminators), but for large stimuli, they do not reflect the global motion direction computed by MT neurons. These observations are consistent with psychophysical findings that show that our perception of moving objects often depends on the motion of terminators.

Our perception of fine visual detail relies on small receptive fields at early stages of visual processing. However, small receptive fields tend to confound the orientation and velocity of moving edges, leading to ambiguous or inaccurate motion measurements (the aperture problem). Thus, it is often assumed that neurons in primary visual cortex (V1) carry only ambiguous motion information. Here we show that a subpopulation of V1 neurons is capable of signaling motion direction in a manner that is independent of contour orientation. Specifically, end-stopped V1 neurons obtain accurate motion measurements by responding only to the endpoints of long contours, a strategy which renders them largely immune to the aperture problem. Furthermore, the time course of end-stopping is similar to the time course of motion integration by MT neurons. These results suggest that cortical neurons might represent object motion by responding selectively to two-dimensional discontinuities in the visual scene.

The analysis of object motion and stereoscopic depth are important tasks that are begun at early stages of the primate visual system. Using sparse white noise, we mapped the receptive field substructure of motion and disparity interactions in neurons in V1 and MT of alert monkeys. Interactions in both regions revealed subunits similar in structure to V1 simple cells. For both motion and stereo, the scale and shape of the receptive field substructure could be predicted from conventional tuning for bars or dot-field stimuli, indicating that the small-scale interactions were repeated across the receptive fields. We also found neurons in V1 and in MT that were tuned to combinations of spatial and temporal binocular disparities, suggesting a possible neural substrate for the perceptual Pulfrich phenomenon. Our observations constrain computational and developmental models of motion-stereo integration.

Normalization has been proposed as a canonical computation operating across different brain regions, sensory modalities, and species. It provides a good phenomenological description of non-linear response properties in primary visual cortex (V1), including the contrast response function and surround suppression. Despite its widespread application throughout the visual system, the underlying neural mechanisms remain largely unknown. We recently observed that corticocortical feedback contributes to surround suppression in V1, raising the possibility that feedback acts through normalization. To test this idea, we characterized area summation and contrast response properties in V1 with and without feedback from V2 and V3 in alert macaques and applied a standard normalization model to the data. Area summation properties were well explained by a form of divisive normalization, which computes the ratio between a neuron's driving input and the spatially integrated activity of a “normalization pool.” Feedback inactivation reduced surround suppression by shrinking the spatial extent of the normalization pool. This effect was independent of the gain modulation thought to mediate the influence of contrast on area summation, which remained intact during feedback inactivation. Contrast sensitivity within the receptive field center was also unaffected by feedback inactivation, providing further evidence that feedback participates in normalization independent of the circuit mechanisms involved in modulating contrast gain and saturation. These results suggest that corticocortical feedback contributes to surround suppression by increasing the visuotopic extent of normalization and, via this mechanism, feedback can play a critical role in contextual information processing.

In order to see the world with high spatial acuity, an animal must sample the visual image with many detectors that restrict their analyses to extremely small regions of space. The visual cortex must then integrate the information from these localized receptive fields to obtain a more global picture of the surrounding environment. We studied this process in single neurons within the middle temporal visual area (MT) of macaques using stimuli that produced conflicting local and global information about stimulus motion. Neuronal responses in alert animals initially reflected predominantly the ambiguous local motion features, but gradually converged to an unambiguous global representation. When the same animals were anaesthetized, the integration of local motion signals was markedly impaired even though neuronal responses remained vigorous and directional tuning characteristics were intact. Our results suggest that anaesthesia preferentially affects the visual processing responsible for integrating local signals into a global visual representation.

Neurons at progressively higher levels of the visual system have progressively larger, more complicated receptive fields, presumably constructed from simpler antecedent receptive fields. To study this hierarchical organization, we used sparse white noise to map receptive-field substructure (second order Wiener-like kernels) in an extrastriate motion processing area (MT) of alert monkeys. The maps revealed a clear substructure, on a spatial scale comparable to the receptive fields of the V1 inputs. There were both facilitatory and suppressive interactions that differed in spatial organization and time course. Directional interactions were remarkably precise over a very small spatial range, and reversed when successive stimuli reversed contrast––a neural correlate of "reverse phi" motion perception. The maps of some cells had an unexpected, curved shape, which challenges existing models for direction selectivity.

To track a moving object, its motion must first be distinguished from that of the background. The center-surround properties of neurons in the middle temporal visual area (MT) may be important for signaling the relative motion between object and background. To test this, we microstimulated within MT and measured the effects on monkeys' eye movements to moving targets. We found that stimulation at "local motion" sites, where receptive fields possessed antagonistic surrounds, shifted pursuit in the preferred direction of the neurons, whereas stimulation at "wide-field motion" sites shifted pursuit in the opposite, or null, direction. We propose that activating wide-field sites simulated background motion, thus inducing a target motion signal in the opposite direction. Our results support the hypothesis that neuronal center-surround mechanisms contribute to the behavioral segregation of objects from the background.

The middle temporal visual area (MT) of the owl monkey is anatomically organized with respect to both preferred direction of motion and different types of center–surround interaction. The latter organization consists of clusters of neurons whose receptive fields have antagonistic surrounds that render them unresponsive to wide-field motion (local motion columns) interdigitated with groups of neurons whose receptive fields have additive surrounds and thus respond best to wide-field motion (wide-field motion columns).To learn whether the information in these regions remained segregated further along the visual pathways, we made injections of retrograde tracers into two visual areas to which MT projects [the medial superior temporal area (MST) and fundus of the superior temporal sulcus (FST)] and then labeled the wide-field and local organization using 2-deoxyglucose. In complementary experiments, we injected anterograde tracers into regions of MT that we had mapped using microelectrode recordings.Injections into both dorsal FST and ventral MST labeled clusters of cell bodies in MT that were concentrated within wide-field motion columns, whereas injections into dorsal MST labeled neurons predominantly within local motion columns. Results from the anterograde tracer experiments corroborated these findings. The high degree of specificity in the connections reinforces a model of functional organization for wide-field versus local motion processing within MT. Our data support the previously reported division of FST into separate dorsal and ventral areas, and they also suggest that MST of the owl monkey is, like MST of the macaque, functionally organized with respect to local versus wide-field motion processing.

To generate behavioral responses based on sensory input, motor areas of the brain must interpret, or “read out,” signals from sensory maps. Our experiments tested several algorithms for how the motor systems for smooth pursuit and saccadic eye movements might extract a usable signal of target velocity from the distributed representation of velocity in the middle temporal visual area (MT or V5). Using microstimulation, we attempted to manipulate the velocity information within MT while monkeys tracked a moving visual stimulus. We examined the effects of the microstimulation on smooth pursuit and on the compensation for target velocity shown by saccadic eye movements. Microstimulation could alter both the speed and direction of the motion estimates of both types of eye movements and could also cause monkeys to generate pursuit even when the visual target was actually stationary. The pattern of alterations suggests that microstimulation can introduce an additional velocity signal into MT and that the pursuit and saccadic systems usually compute a vector average of the visually evoked and microstimulation-induced velocity signals (pursuit, 55 of 122 experiments; saccades, 70 of 122). Microstimulation effects in a few experiments were consistent with vector summation of these two signals (pursuit, 6 of 122; saccades, 2 of 122). In the remainder of the experiments, microstimulation caused either an apparent impairment in motion processing (pursuit, 47 of 122; saccades, 41 of 122) or had no effect (pursuit, 14 of 122; saccades, 9 of 122). Within individual experiments, the effects on pursuit and saccades were usually similar, but the occasional striking exception suggests that the two eye movement systems may perform motion computations somewhat independently.

Neurons in sensory cortical areas are tuned to multiple dimensions, or features, of their sensory space. Understanding how single neurons represent multiple features is of great interest for determining the informative dimensions of the neurons' response, the decoding algorithms appropriate for extracting this information from the neuronal population, and for determining where specific transformations occur along the visual hierarchy. Despite the established role of cortical area MT in judgments of motion and depth, it is not known how individual neurons jointly encode the two dimensions. We investigated the joint tuning of individual MT neurons for two visual features: direction of motion and binocular disparity, an important depth cue. We found that a separable, multiplicative combination of tuning for the two features can account for more than 90% of the variance in the joint tuning function for over 91% of MT neurons. These results suggest 1) that each feature can be read out independently from MT by simply averaging across the population without regard to the other feature and 2) that the inseparable representations seen in subsequent areas, such as MST, must be computed beyond MT. Intriguingly, we found that the remaining nonseparable component of the joint tuning function often manifested as small but systematic changes in the neurons' preferences for one feature as the other one was varied. We believe this reflects the local columnar organization of tuning for direction and binocular disparity in MT, indicating that joint tuning may provide a new tool with which to probe functional architecture.

Feedback connections are prevalent throughout the cerebral cortex, yet their function remains poorly understood. Previous studies in anesthetized monkeys found that inactivating feedback from extrastriate visual cortex produced effects in striate cortex that were relatively weak, generally suppressive, largest for visual stimuli confined to the receptive field center, and detectable only at low stimulus contrast. We studied the influence of corticocortical feedback in alert monkeys using cortical cooling to reversibly inactivate visual areas 2 (V2) and 3 (V3) while characterizing receptive field properties in primary visual cortex (V1). We show that inactivation of feedback from areas V2 and V3 results in both response suppression and facilitation for stimuli restricted to the receptive field center, in most cases leading to a small reduction in the degree of orientation selectivity but no change in orientation preference. For larger-diameter stimuli that engage regions beyond the center of the receptive field, eliminating feedback from V2 and V3 results in strong and consistent response facilitation, effectively reducing the strength of surround suppression in V1 for stimuli of both low and high contrast. For extended contours, eliminating feedback had the effect of reducing end stopping. Inactivation effects were largest for neurons that exhibited strong surround suppression before inactivation, and their timing matched the dynamics of surround suppression under control conditions. Our results provide direct evidence that feedback contributes to surround suppression, which is an important source of contextual influences essential to vision.

In order to correctly determine the velocity of moving objects, the brain must integrate information derived from a large number of local detectors. The geometry of objects, the presence of occluding surfaces and the restricted receptive fields of early motion detectors conspire to render many of these measurements unreliable. One possible solution to this problem, often referred to as the “aperture problem,” involves differential weighting of local cues according to their fidelity: measurements made near two-dimensional object features called “terminators” are selectively integrated, whereas one-dimensional motion signals emanating from object contours are given less weight. A large number of experiments have assessed the integration of these different kinds of motion cues using perceptual reports, eye movements and neuronal activity. All of the results show striking qualitative similarities in the temporal sequence of integration: the earliest responses reveal a non-selective integration which becomes progressively selective over a period of time. In this chapter we propose a simple mechanistic model based on end-stopped, direction-selective neurons in V1 of the macaque, and use it to account for the dynamics observed in perception, eye movements, and neural responses in MT.

Motion sensitivity is a fundamental aspect of vision allowing the tracking, avoidance and interception of objects, the extraction of depth information, and the calibration of proprioception and locomotion. Motion sensitivity is a primary feature of neurons throughout the dorsal stream of visual cortex, but neurons in the middle temporal area (MT) have been most closely linked to the perception of direction, speed and depth. This section introduces the fundamental requirements of motion detection and then focuses on how the primate visual system encodes the properties of visual motion, and how this encoding ultimately affects perception and behavior.

2-Deoxyglucose experiments have raised the possibility of a functional organization for spatial frequency in macaque striate cortex. To analyze this possibility with better spatial resolution, we made tangential microelectrode penetrations at constant eccentricity through supragranular striate cortex in 7 anesthetized, paralyzed macaque monkeys. We recorded from 121 single units. The data fell into two distinct populations with respect to mean preferred spatial frequency: (i) interblob cells (3.8 +/- 2.0 cycles/degree, n = 83) and (ii) blob cells (1.1 +/- 0.8 cycles/degree, n = 38; P less than 0.001). Beyond this, we found no evidence for an orderly mapping of spatial frequency optima. At blob-interblob borders, we observed abrupt shifts from low, relatively uniform spatial frequency optima (blobs) to higher optima that varied unsystematically (interblobs). The spatial frequency optima (low vs. high) and nature of the tuning curves (low-pass vs. band-pass) in blob vs. interblob cells correlate well with psychophysical measures of the same differences for the chrominance vs. luminance channels. These data are consistent with a functional subdivision of striate cortex in which blob cells carry information concerned predominantly with color and interblob neurons carry information important for form analysis.

An important part of visual perception is our ability to measure the speed and direction of moving objects. But what do we actually see when we see something move? The question is surprisingly difficult to answer, because our conscious experience of motion is actually quite different from the motion information signaled by many of the neurons in the visual cortex. The disparity results from a counterintuitive fact imposed by geometry: any motion detector with a limited field of view will make systematic errors when measuring the motion of large objects. Because neurons at early stages of the visual system have small receptive fields, these errors are extremely common. Computational modeling has suggested a number of candidate strategies by which the visual system could overcome inaccurate measurements of motion. These models invariably posit a first stage that performs local motion measurements, and a second stage that combines local motion measurements to achieve an accurate reading of velocity. In some of the models, which we call integrationist, the second stage performs all of the computations necessary to resolve the inaccuracies present in the first stage. In contrast, selectionist models hypothesize that neurons in the first stage respond selectively to the most informative regions of the input, where local motion measurements are most likely to be accurate. Although the two types of model are very similar in their fundamental computations, they differ substantively in the roles they assign to different parts of the visual cortex. Of the many different cortical regions devoted to vision, the two most thoroughly studied are the primary visual cortex (V1) and the middle temporal area (MT). In most motion models, it is assumed that V1 measures motion inaccurately and MT performs certain calculations on the V1 output to obtain the correct velocity. These assumptions are based on electrophysiological recordings from both areas, which have often supported the integrationist approach. At the same time a number of results on human perception and single-neuron recording are more consistent with a selectionist approach, and a number of models have been developed along these lines. This chapter reviews many of the existing computational models, along with the relevant perceptual and electrophysiological data. In particular, we describe the relationship of cortical areas V1 and MT to the calculations hypothesized by the various models.

Processing of visual information is both parallel and hierarchical, with each visual area richly interconnected with other visual areas. An example of the parallel architecture of the primate visual system is the existence of two principal pathways providing input to the middle temporal visual area (MT): namely, a direct projection from striate cortex (V1), and a set of indirect projections that also originate in V1 but then relay through V2 and V3. Here we have reversibly inactivated the indirect pathways while recording from MT neurons and measuring eye movements in alert monkeys, a procedure that has enabled us to assess whether the two different input pathways are redundant or whether they carry different kinds of information. We find that this inactivation causes a disproportionate degradation of binocular disparity tuning relative to direction tuning in MT neurons, suggesting that the indirect pathways are important in the recovery of depth in three-dimensional scenes.

Since the introduction of diffusion weighted imaging (DWI) as a method for examining neural connectivity, its accuracy has not been formally evaluated. In this study, we directly compared connections that were visualized using injected neural tract tracers (WGA-HRP) with those obtained using in-vivo diffusion tensor imaging (DTI) tractography. First, we injected the tracer at multiple sites in the brain of a macaque monkey; second, we reconstructed the histological sections of the labeled fiber tracts in 3D; third, we segmented and registered the fibers (somatosensory and motor tracts) with the anatomical in-vivo MRI from the same animal; and last, we conducted fiber tracing along the same pathways on the DTI data using a classical diffusion tracing technique with the injection sites as seeds. To evaluate the performance of DTI fiber tracing, we compared the fibers derived from the DTI tractography with those segmented from the histology. We also studied the influence of the parameters controlling the tractography by comparing Dice superimposition coefficients between histology and DTI segmentations. While there was generally good visual agreement between the two methods, our quantitative comparisons reveal certain limitations of DTI tractography, particularly for regions at remote locations from seeds. We have thus demonstrated the importance of appropriate settings for realistic tractography results.

The small visual area known as MT or V5 has played a major role in our understanding of the primate cerebral cortex. This area has been historically important in the concept of cortical processing streams and the idea that different visual areas constitute highly specialized representations of visual information. MT has also proven to be a fertile culture dish—full of direction- and disparity-selective neurons—exploited by many labs to study the neural circuits underlying computations of motion and depth and to examine the relationship between neural activity and perception. Here we attempt a synthetic overview of the rich literature on MT with the goal of answering the question, What does MT do?

Visual neurons are often characterized in terms of their tuning for various stimulus properties, such as shape, color, and velocity. Generally, these tuning curves are further modulated by the overall intensity of the stimulus, such that increasing the contrast increases the firing rate, up to some maximum. In this paper, we describe the tuning of neurons in the middle temporal area (MT or V5) of macaque visual cortex for moving stimuli of varying contrast. We find that, for some MT neurons, tuning curves for stimulus direction, speed, and size are shaped in part by suppressive influences that are present at high stimulus contrast but weak or nonexistent at low contrast. For most neurons, the suppression is direction-specific and strongest for large, slow-moving stimuli. The surprising consequence of this phenomenon is that some MT neurons actually fire more vigorously to a large low-contrast stimulus than to one of high contrast. These results are consistent with recent perceptual observations, as well as with information-theoretic models, which hypothesize that the visual system seeks to reduce redundancy at high contrast while maintaining sensitivity at low contrast.

To clearly see a moving object, an observer must rotate his or her eyes with a velocity that matches that of the object. Such rotations are called smooth pursuit eye movements, and they depend critically on the ability of the primate brain to integrate information about object velocity from various local motion signals. When the local motion signals are in conflict, it is possible to use smooth pursuit eye movements as a continuous read-out of the motion integration process. This review discusses the results of recent behavioral experiments that have taken this approach, along with relevant neurophysiological and computational studies.

Our brains focus on important events and filter out distracting ones. An investigation in monkeys reveals a surprising dissociation between the neuronal and behavioural manifestations of attention. See Letter p.434 When we pay attention to certain locations, the responses of neurons in visual areas that represent those locations are enhanced. Here, Alexandre Zenon and Richard Krauzlis show that in adult rhesus monkeys, inactivation of the superior colliculus — part of the midbrain associated with eye movements — affects the perceptual effects of attention, but does not seem to affect the attentional modulation of upstream visual areas. Most work on attention is based on the idea that perceptual enhancement is driven by modulation of the visual response. This work suggests that visual attention involves other aspects of neuronal activity in the visual cortical areas — or perhaps other brain areas not directly associated with vision.

How does the brain combine local motion measurements to form an accurate description of object motion? For example, if a vertically oriented bar moves upward and rightward at a constant velocity, a neuron with a small receptive field positioned along the length of the contour can measure only the rightward component of motion, since the upward component provides no time-varying information within its receptive field. In contrast, cells positioned at the endpoints of the contour can measure motion direction accurately. Because direction-selective neurons early in the visual pathways have small receptive fields, the visual system is constantly faced with this “aperture problem.”1 And since they provide the sole input to subsequent stages of cortical visual processing, which in turn inform the premotor circuitry used for making eye movements, the error in measuring local velocity could be perpetuated. How are these conflicting motion signals—the potentially erroneous signals measured along a contour and the correct signals originating from terminators—ultimately resolved in the visual cortex? Microelectrode recordings from neurons in the middle temporal visual area (MT) of alert monkeys have shown that the earliest directional responses, beginning about 80 msec after the onset of stimulus motion, primarily encode the component of motion perpendicular to the orientation of a contour. That is, they are strongly affected by the ambiguous contour signals. However, the later responses (>140 msec after motion onset) encode the true direction of motion, irrespective of contour orientation. Thus the responses of MT neurons reflect the solution of the aperture problem for motion over a period of about 60 msec.2 Given the evidence that MT neuronal signals are important for the initiation of smooth pursuit eye movements,3–5 we asked whether the time-evolving signals we observed in MT neurons had a behavioral correlate. We had subjects (monkeys and humans) track the center of a single moving bar whose orientation was varied with respect to its direction of motion. The bar was dim green (4.2 cd · m−2; u′ = 0.28, v′ = 0.59), and its center was indicated by an isoluminant, red gaussian blob

To clearly see a moving object, an observer must rotate his or her eyes with a velocity that matches that of the object. Such rotations are called smooth pursuit eye movements, and they depend critically on the ability of the primate brain to integrate information about object velocity from various local motion signals. When the local motion signals are in conflict, it is possible to use smooth pursuit eye movements as a continuous read-out of the motion integration process. This review discusses the results of recent behavioral experiments that have taken this approach, along with relevant neurophysiological and computational studies.

The mammalian visual system processes many different aspects of the visual scene in separate, parallel channels. Recent experiments suggest that the visual cortex, like the retina, forms parallel circuits even at very fine spatial scales.

A critical step in the interpretation of the visual world is the integration of the various local motion signals generated by moving objects. This process is complicated by the fact that local velocity measurements can differ depending on contour orientation and spatial position. Specifically, any local motion detector can measure only the component of motion perpendicular to a contour that extends beyond its field of view. This "aperture problem" is particularly relevant to direction-selective neurons early in the visual pathways, where small receptive fields permit only a limited view of a moving object. Here we show that neurons in the middle temporal visual area (known as MT or V5) of the macaque brain reveal a dynamic solution to the aperture problem. MT neurons initially respond primarily to the component of motion perpendicular to a contour's orientation, but over a period of approximately 60 ms the responses gradually shift to encode the true stimulus direction, regardless of orientation. We also report a behavioural correlate of these neural responses: the initial velocity of pursuit eye movements deviates in a direction perpendicular to local contour orientation, suggesting that the earliest neural responses influence the oculomotor response.