Spatial precision of population activity in primate area MT

Marmosets: a promising model for probing the neural mechanisms underlying complex visual networks such as the frontal-parietal network

The technology, methodology and models used by visual neuroscientists have provided great insights into the structure and function of individual brain areas. However, complex cognitive functions arise in the brain due to networks comprising multiple interacting cortical areas that are wired together with precise anatomical connections. A prime example of this phenomenon is the frontal–parietal network and two key regions within it: the frontal eye fields (FEF) and lateral intraparietal area (area LIP). Activity in these cortical areas has independently been tied to oculomotor control, motor preparation, visual attention and decision-making. Strong, bidirectional anatomical connections have also been traced between FEF and area LIP, suggesting that the aforementioned visual functions depend on these inter-area interactions. However, advancements in our knowledge about the interactions between area LIP and FEF are limited with the main animal model, the rhesus macaque, because these key regions are buried in the sulci of the brain. In this review, we propose that the common marmoset is the ideal model for investigating how anatomical connections give rise to functionally-complex cognitive visual behaviours, such as those modulated by the frontal–parietal network, because of the homology of their cortical networks with humans and macaques, amenability to transgenic technology, and rich behavioural repertoire. Furthermore, the lissencephalic structure of the marmoset brain enables application of powerful techniques, such as array-based electrophysiology and optogenetics, which are critical to bridge the gaps in our knowledge about structure and function in the brain.

Spatial resolution of local field potential signals in macaque V4

OBJECTIVE

An important challenge for the development of cortical visual prostheses is to generate spatially localized percepts of light, using artificial stimulation. Such percepts are called phosphenes, and the goal of prosthetic applications is to generate a pattern of phosphenes that matches the structure of the retinal image. A preliminary step in this process is to understand how the spatial positions of phosphene-like visual stimuli are encoded in the distributed activity of cortical neurons. The spatial resolution with which the distributed responses discriminate positions puts a limit on the capability of visual prosthesis devices to induce phosphenes at multiple positions. While most previous prosthetic devices have targeted the primary visual cortex, the extrastriate cortex has the advantage of covering a large part of the visual field with a smaller amount of cortical tissue, providing the possibility of a more compact implant. Here, we studied how well ensembles of Local Field Potentials (LFPs) and Multiunit activity (MUA) responses from extrastriate cortical visual area V4 of a behaving macaque monkey can discriminate between two-dimensional spatial positions.

APPROACH

We used support vector machines (SVM) to determine the capabilities of LFPs and MUA to discriminate responses to phosphene-like stimuli (probes) at different spatial separations. We proposed a selection strategy based on the combined responses of multiple electrodes and used the linear learning weights to find the minimum number of electrodes for fine and coarse discriminations. We also measured the contribution of correlated trial-to-trial variability in the responses to the discrimination performance for MUA and LFP.

MAIN RESULTS

We found that despite the large receptive field sizes in V4, the combined responses from multiple sites, whether MUA or LFP, are capable of fine and coarse discrimination of positions. Our electrode selection procedure significantly increased discrimination performance while reducing the required number of electrodes. Analysis of noise correlations in MUA and LFP responses showed that noise correlations in LFPs carry more information about spatial positions.

SIGNIFICANCE

This study determined the coding strategy for fine discrimination, suggesting that spatial positions could be well localized with patterned stimulation in extrastriate area V4. It also provides a novel approach to build a compact prosthesis with relatively few electrodes, which has the potential advantage of reducing tissue damage in real applications.

Visual Motion Processing in Macaque V2

In the primate visual system, direction-selective (DS) neurons are critical for visual motion perception. While DS neurons in the dorsal visual pathway have been well characterized, the response properties of DS neurons in other major visual areas are largely unexplored. Recent optical imaging studies in monkey visual cortex area 2 (V2) revealed clusters of DS neurons. This imaging method facilitates targeted recordings from these neurons. Using optical imaging and single-cell recording, we characterized detailed response properties of DS neurons in macaque V2. Compared with DS neurons in the dorsal areas (e.g., middle temporal area [MT]), V2 DS neurons have a smaller receptive field and a stronger antagonistic surround. They do not code speed or plaid motion but are sensitive to motion contrast. Our results suggest that V2 DS neurons play an important role in figure-ground segregation. The clusters of V2 DS neurons are likely specialized functional systems for detecting motion contrast.

Defined Cell Types in Superior Colliculus Make Distinct Contributions to Prey Capture Behavior in the Mouse

The superior colliculus (SC) plays a highly conserved role in visual processing and mediates visual orienting behaviors across species, including both overt motor orienting [1, 2] and orienting of attention [3, 4]. To determine the specific circuits within the superficial superior colliculus (sSC) that drive orienting and approach behavior toward appetitive stimuli, we explored the role of three genetically defined cell types in mediating prey capture in mice. Chemogenetic inactivation of two classically defined cell types, the wide-field (WF) and narrow-field (NF) vertical neurons, revealed that they are involved in distinct aspects of prey capture. WF neurons were required for rapid prey detection and distant approach initiation, whereas NF neurons were required for accurate orienting during pursuit as well as approach initiation and continuity. In contrast, prey capture did not require parvalbumin-expressing (PV) neurons that have previously been implicated in fear responses. The visual coding and projection targets of WF and NF cells were consistent with their roles in prey detection versus pursuit, respectively. Thus, our studies link specific neural circuit connectivity and function with stimulus detection and orienting behavior, providing insight into visuomotor and attentional mechanisms mediated by superior colliculus.

Population receptive field estimates for motion-defined stimuli

The processing of motion changes throughout the visual hierarchy, from spatially restricted ‘local motion’ in early visual cortex to more complex large-field ‘global motion’ at later stages. Here we used functional magnetic resonance imaging (fMRI) to examine spatially selective responses in these areas related to the processing of random-dot stimuli defined by differences in motion. We used population receptive field (pRF) analyses to map retinotopic cortex using bar stimuli comprising coherently moving dots. In the first experiment, we used three separate background conditions: no background dots (dot-defined bar-only), dots moving coherently in the opposite direction to the bar (kinetic boundary) and dots moving incoherently in random directions (global motion). Clear retinotopic maps were obtained for the bar-only and kinetic-boundary conditions across visual areas V1–V3 and in higher dorsal areas. For the global-motion condition, retinotopic maps were much weaker in early areas and became clear only in higher areas, consistent with the emergence of global-motion processing throughout the visual hierarchy. However, in a second experiment we demonstrate that this pattern is not specific to motion-defined stimuli, with very similar results for a transparent-motion stimulus and a bar defined by a static low-level property (dot size) that should have driven responses particularly in V1. We further exclude explanations based on stimulus visibility by demonstrating that the observed differences in pRF properties do not follow the ability of observers to localise or attend to these bar elements. Rather, our findings indicate that dorsal extrastriate retinotopic maps may primarily be determined by the visibility of the neural responses to the bar relative to the background response (i.e. neural signal-to-noise ratios) and suggests that claims about stimulus selectivity from pRF experiments must be interpreted with caution.

Weighting neurons by selectivity produces near-optimal population codes

Perception is produced by "reading out" the representation of a sensory stimulus contained in the activity of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly weighted sum of the neurons' spike counts. This approach is popular because of the biological plausibility of weighted, nonlinear integration. For neurons recorded in vivo, weights are highly variable when derived through optimization methods, but it is unclear how the variability affects decoding performance in practice. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets (Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in 1 of 12 different directions. We found that high peak response and direction selectivity both predicted that a neuron would be weighted more highly in an optimized decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron's tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron's preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights. NEW & NOTEWORTHY We examined which aspects of a neuron's tuning account for its contribution to sensory coding. Strongly direction-selective neurons are weighted most highly by optimal decoders trained to discriminate motion direction. Models with predefined decoding weights demonstrate that this weighting scheme causally improved direction representation by a neuronal population. Optimizing decoders (using a generalized linear model or Fisher's linear discriminant) led to only marginally better performance than decoders based purely on a neuron's preferred direction and selectivity.

Axonal Projections From the Middle Temporal Area in the Common Marmoset

Neural activity in the middle temporal (MT) area is modulated by the direction and speed of motion of visual stimuli. The area is buried in a sulcus in the macaque, but exposed to the cortical surface in the marmoset, making the marmoset an ideal animal model for studying MT function. To better understand the details of the roles of this area in cognition, underlying anatomical connections need to be clarified. Because most anatomical tracing studies in marmosets have used retrograde tracers, the axonal projections remain uncharacterized. In order to examine axonal projections from MT, we utilized adeno-associated viral (AAV) tracers, which work as anterograde tracers by expressing either green or red fluorescent protein in infected neurons. AAV tracers were injected into three sites in MT based on retinotopy maps obtained via in vivo optical intrinsic signal imaging. Brains were sectioned and divided into three series, one for fluorescent image scanning and two for myelin and Nissl substance staining to identify specific brain areas. Overall projection patterns were similar across the injections. MT projected to occipital visual areas V1, V2, V3 (VLP) and V4 (VLA) and surrounding areas in the temporal cortex including MTC (V4T), MST, FST, FSTv (PGa/IPa) and TE3. There were also projections to the dorsal visual pathway, V3A (DA), V6 (DM) and V6A, the intraparietal areas AIP, LIP, MIP, frontal A4ab and the prefrontal cortex, A8aV and A8C. There was a visuotopic relationship with occipital visual areas. In a marmoset in which two tracer injections were made, the projection targets did not overlap in A8aV and AIP, suggesting topographic projections from different parts of MT. Most of these areas are known to send projections back to MT, suggesting that they are reciprocally connected with it.

A video-driven model of response statistics in the primate middle temporal area

Neurons in the primate middle temporal area (MT) encode information about visual motion and binocular disparity. MT has been studied intensively for decades, so there is a great deal of information in the literature about MT neuron tuning. In this study, our goal is to consolidate some of this information into a statistical model of the MT population response. The model accepts arbitrary stereo video as input. It uses computer-vision methods to calculate known correlates of the responses (such as motion velocity), and then predicts activity using a combination of tuning functions that have previously been used to describe data in various experiments. To construct the population response, we also estimate the distributions of many model parameters from data in the electrophysiology literature. We show that the model accounts well for a separate dataset of MT speed tuning that was not used in developing the model. The model may be useful for studying relationships between MT activity and behavior in ethologically relevant tasks. As an example, we show that the model can provide regression targets for internal activity in a deep convolutional network that performs a visual odometry task, so that its representations become more physiologically realistic.

Visual Motion Discrimination by Propagating Patterns in Primate Cerebral Cortex

Visual stimuli can evoke waves of neural activity that propagate across the surface of visual cortical areas. The relevance of these waves for visual processing is unknown. Here, we measured the phase and amplitude of local field potentials (LFPs) in electrode array recordings from the motion-processing medial temporal (MT) area of anesthetized male marmosets. Animals viewed grating or dot-field stimuli drifting in different directions. We found that, on individual trials, the direction of LFP wave propagation is sensitive to the direction of stimulus motion. Propagating LFP patterns are also detectable in trial-averaged activity, but the trial-averaged patterns exhibit different dynamics and behaviors from those in single trials and are similar across motion directions. We show that this difference arises because stimulus-sensitive propagating patterns are present in the phase of single-trial oscillations, whereas the trial-averaged signal is dominated by additive amplitude effects. Our results demonstrate that propagating LFP patterns can represent sensory inputs at timescales relevant to visually guided behaviors and raise the possibility that propagating activity patterns serve neural information processing in area MT and other cortical areas.SIGNIFICANCE STATEMENT Propagating wave patterns are widely observed in the cortex, but their functional relevance remains unknown. We show here that visual stimuli generate propagating wave patterns in local field potentials (LFPs) in a movement-sensitive area of the primate cortex and that the propagation direction of these patterns is sensitive to stimulus motion direction. We also show that averaging LFP signals across multiple stimulus presentations (trial averaging) yields propagating patterns that capture different dynamic properties of the LFP response and show negligible direction sensitivity. Our results demonstrate that sensory stimuli can modulate propagating wave patterns reliably in the cortex. The relevant dynamics are normally masked by trial averaging, which is a conventional step in LFP signal processing.

Dynamic population codes of multiplexed stimulus features in primate area MT

The middle-temporal area (MT) of primate visual cortex is critical in the analysis of visual motion. Single-unit studies suggest that the response dynamics of neurons within area MT depend on stimulus features, but how these dynamics emerge at the population level, and how feature representations interact, is not clear. Here, we used multivariate classification analysis to study how stimulus features are represented in the spiking activity of populations of neurons in area MT of marmoset monkey. Using representational similarity analysis we distinguished the emerging representations of moving grating and dot field stimuli. We show that representations of stimulus orientation, spatial frequency, and speed are evident near the onset of the population response, while the representation of stimulus direction is slower to emerge and sustained throughout the stimulus-evoked response. We further found a spatiotemporal asymmetry in the emergence of direction representations. Representations for high spatial frequencies and low temporal frequencies are initially orientation dependent, while those for high temporal frequencies and low spatial frequencies are more sensitive to motion direction. Our analyses reveal a complex interplay of feature representations in area MT population response that may explain the stimulus-dependent dynamics of motion vision.NEW & NOTEWORTHY Simultaneous multielectrode recordings can measure population-level codes that previously were only inferred from single-electrode recordings. However, many multielectrode recordings are analyzed using univariate single-electrode analysis approaches, which fail to fully utilize the population-level information. Here, we overcome these limitations by applying multivariate pattern classification analysis and representational similarity analysis to large-scale recordings from middle-temporal area (MT) in marmoset monkeys. Our analyses reveal a dynamic interplay of feature representations in area MT population response.

Neural plasticity following lesions of the primate occipital lobe: The marmoset as an animal model for studies of blindsight

For nearly a century it has been observed that some residual visually guided behavior can persist after damage to the primary visual cortex (V1) in primates. The age at which damage to V1 occurs leads to different outcomes, with V1 lesions in infancy allowing better preservation of visual faculties in comparison with those incurred in adulthood. While adult V1 lesions may still allow retention of some limited visual abilities, these are subconscious-a characteristic that has led to this form of residual vision being referred to as blindsight. The neural basis of blindsight has been of great interest to the neuroscience community, with particular focus on understanding the contributions of the different subcortical pathways and cortical areas that may underlie this phenomenon. More recently, research has started to address which forms of neural plasticity occur following V1 lesions at different ages, including work using marmoset monkeys. The relatively rapid postnatal development of this species, allied to the lissencephalic brains and well-characterized visual cortex provide significant technical advantages, which allow controlled experiments exploring visual function in the absence of V1. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 314-327, 2017.

Distributed and Dynamic Neural Encoding of Multiple Motion Directions of Transparently Moving Stimuli in Cortical Area MT

Segmenting visual scenes into distinct objects and surfaces is a fundamental visual function. To better understand the underlying neural mechanism, we investigated how neurons in the middle temporal cortex (MT) of macaque monkeys represent overlapping random-dot stimuli moving transparently in slightly different directions. It has been shown that the neuronal response elicited by two stimuli approximately follows the average of the responses elicited by the constituent stimulus components presented alone. In this scheme of response pooling, the ability to segment two simultaneously presented motion directions is limited by the width of the tuning curve to motion in a single direction. We found that, although the population-averaged neuronal tuning showed response averaging, subgroups of neurons showed distinct patterns of response tuning and were capable of representing component directions that were separated by a small angle--less than the tuning width to unidirectional stimuli. One group of neurons preferentially represented the component direction at a specific side of the bidirectional stimuli, weighting one stimulus component more strongly than the other. Another group of neurons pooled the component responses nonlinearly and showed two separate peaks in their tuning curves even when the average of the component responses was unimodal. We also show for the first time that the direction tuning of MT neurons evolved from initially representing the vector-averaged direction of slightly different stimuli to gradually representing the component directions. Our results reveal important neural processes underlying image segmentation and suggest that information about slightly different stimulus components is computed dynamically and distributed across neurons.

SIGNIFICANCE STATEMENT

Natural scenes often contain multiple entities. The ability to segment visual scenes into distinct objects and surfaces is fundamental to sensory processing and is crucial for generating the perception of our environment. Because cortical neurons are broadly tuned to a given visual feature, segmenting two stimuli that differ only slightly is a challenge for the visual system. In this study, we discovered that many neurons in the visual cortex are capable of representing individual components of slightly different stimuli by selectively and nonlinearly pooling the responses elicited by the stimulus components. We also show for the first time that the neural representation of individual stimulus components developed over a period of ∼70-100 ms, revealing a dynamic process of image segmentation.