Rapid Adaptation Induces Persistent Biases in Population Codes for Visual Motion

Adaptation in the visual system: Networked fatigue or suppressed prediction error signalling?

Our brains are constantly adapting to changes in our visual environments. Neural adaptation exerts a persistent influence on the activity of sensory neurons and our perceptual experience, however there is a lack of consensus regarding how adaptation is implemented in the visual system. One account describes fatigue-based mechanisms embedded within local networks of stimulus-selective neurons (networked fatigue models). Another depicts adaptation as a product of stimulus expectations (predictive coding models). In this review, I evaluate neuroimaging and psychophysical evidence that poses fundamental problems for predictive coding models of neural adaptation. Specifically, I discuss observations of distinct repetition and expectation effects, as well as incorrect predictions of repulsive adaptation aftereffects made by predictive coding accounts. Based on this evidence, I argue that networked fatigue models provide a more parsimonious account of adaptation effects in the visual system. Although stimulus expectations can be formed based on recent stimulation history, any consequences of these expectations are likely to co-occur (or interact) with effects of fatigue-based adaptation. I conclude by proposing novel, testable hypotheses relating to interactions between fatigue-based adaptation and other predictive processes, focusing on stimulus feature extrapolation phenomena.

Expectation violations enhance neuronal encoding of sensory information in mouse primary visual cortex

The response of cortical neurons to sensory stimuli is shaped both by past events (adaptation) and the expectation of future events (prediction). Here we employed a visual stimulus paradigm with different levels of predictability to characterise how expectation influences orientation selectivity in the primary visual cortex (V1) of male mice. We recorded neuronal activity using two-photon calcium imaging (GCaMP6f) while animals viewed sequences of grating stimuli which either varied randomly in their orientations or rotated predictably with occasional transitions to an unexpected orientation. For single neurons and the population, there was significant enhancement in the gain of orientation-selective responses to unexpected gratings. This gain-enhancement for unexpected stimuli was prominent in both awake and anaesthetised mice. We implemented a computational model to demonstrate how trial-to-trial variability in neuronal responses were best characterised when adaptation and expectation effects were combined.

Calibrating Vision: Concepts and Questions

The idea that visual coding and perception are shaped by experience and adjust to changes in the environment or the observer is universally recognized as a cornerstone of visual processing, yet the functions and processes mediating these calibrations remain in many ways poorly understood. In this article we review a number of facets and issues surrounding the general notion of calibration, with a focus on plasticity within the encoding and representational stages of visual processing. These include how many types of calibrations there are - and how we decide; how plasticity for encoding is intertwined with other principles of sensory coding; how it is instantiated at the level of the dynamic networks mediating vision; how it varies with development or between individuals; and the factors that may limit the form or degree of the adjustments. Our goal is to give a small glimpse of an enormous and fundamental dimension of vision, and to point to some of the unresolved questions in our understanding of how and why ongoing calibrations are a pervasive and essential element of vision.

Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System

Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures.

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.

Motion Perception in the Common Marmoset

Visual motion processing is a well-established model system for studying neural population codes in primates. The common marmoset, a small new world primate, offers unparalleled opportunities to probe these population codes in key motion processing areas, such as cortical areas MT and MST, because these areas are accessible for imaging and recording at the cortical surface. However, little is currently known about the perceptual abilities of the marmoset. Here, we introduce a paradigm for studying motion perception in the marmoset and compare their psychophysical performance with human observers. We trained two marmosets to perform a motion estimation task in which they provided an analog report of their perceived direction of motion with an eye movement to a ring that surrounded the motion stimulus. Marmosets and humans exhibited similar trade-offs in speed versus accuracy: errors were larger and reaction times were longer as the strength of the motion signal was reduced. Reverse correlation on the temporal fluctuations in motion direction revealed that both species exhibited short integration windows; however, marmosets had substantially less nondecision time than humans. Our results provide the first quantification of motion perception in the marmoset and demonstrate several advantages to using analog estimation tasks.

Enhanced Sensory Coding in Mouse Vibrissal and Visual Cortex through TRPA1

Transient receptor potential ankyrin 1 (TRPA1) is a non-selective cation channel, broadly expressed throughout the body. Despite its expression in the mammalian brain, little is known about the contribution of TRPA1 to cortical function. Here, we characterize how TRPA1 affects sensory information processing in two cortical areas in mice: the primary vibrissal (whisker) somatosensory cortex (vS1) and the primary visual cortex (V1). In vS1, local activation of TRPA1 by allyl isothiocyanate (AITC) increases the ongoing activity of neurons and their evoked response to vibrissal stimulation, producing a positive gain modulation. The gain modulation is reversed by TRPA1 inhibitor HC-030031 and is absent in TRPA1 knockout mice. Similarly, in V1, TRPA1 activation increases the gain of direction and orientation selectivity. Linear decoding of V1 population activity confirms faster and more reliable encoding of visual signals under TRPA1 activation. Overall, our findings reveal a physiological role for TRPA1 in enhancing sensory signals in the mammalian cortex.

Histology-Based Average Template of the Marmoset Cortex With Probabilistic Localization of Cytoarchitectural Areas

The rapid adoption of marmosets in neuroscience has created a demand for three dimensional (3D) atlases of the brain of this species to facilitate data integration in a common reference space. We report on a new open access template of the marmoset cortex (the Nencki-Monash, or NM template), representing a morphological average of 20 brains of young adult individuals, obtained by 3D reconstructions generated from Nissl-stained serial sections. The method used to generate the template takes into account morphological features of the individual brains, as well as the borders of clearly defined cytoarchitectural areas. This has resulted in a resource which allows direct estimates of the most likely coordinates of each cortical area, as well as quantification of the margins of error involved in assigning voxels to areas, and preserves quantitative information about the laminar structure of the cortex. We provide spatial transformations between the NM and other available marmoset brain templates, thus enabling integration with magnetic resonance imaging (MRI) and tracer-based connectivity data. The NM template combines some of the main advantages of histology-based atlases (e.g. information about the cytoarchitectural structure) with features more commonly associated with MRI-based templates (isotropic nature of the dataset, and probabilistic analyses). The underlying workflow may be found useful in the future development of 3D brain atlases that incorporate information about the variability of areas in species for which it may be impractical to ensure homogeneity of the sample in terms of age, sex and genetic background.

Low rank mechanisms underlying flexible visual representations

Neuronal population responses to sensory stimuli are remarkably flexible. The responses of neurons in visual cortex have heterogeneous dependence on stimulus properties (e.g., contrast), processes that affect all stages of visual processing (e.g., adaptation), and cognitive processes (e.g., attention or task switching). Understanding whether these processes affect similar neuronal populations and whether they have similar effects on entire populations can provide insight into whether they utilize analogous mechanisms. In particular, it has recently been demonstrated that attention has low rank effects on the covariability of populations of visual neurons, which impacts perception and strongly constrains mechanistic models. We hypothesized that measuring changes in population covariability associated with other sensory and cognitive processes could clarify whether they utilize similar mechanisms or computations. Our experimental design included measurements in multiple visual areas using four distinct sensory and cognitive processes. We found that contrast, adaptation, attention, and task switching affect the variability of responses of populations of neurons in primate visual cortex in a similarly low rank way. These results suggest that a given circuit may use similar mechanisms to perform many forms of modulation and likely reflects a general principle that applies to a wide range of brain areas and sensory, cognitive, and motor processes.

Magnitude, time course, and specificity of rapid adaptation across mouse visual areas

Adaptation is a ubiquitous feature of sensory processing whereby recent experience shapes future responses. The mouse primary visual cortex (V1) is particularly sensitive to recent experience, where a brief stimulus can suppress subsequent responses for seconds. This rapid adaptation profoundly impacts perception, suggesting that its effects are propagated along the visual hierarchy. To understand how rapid adaptation influences sensory processing, we measured its effects at key nodes in the visual system: in V1, three higher visual areas (HVAs: lateromedial, anterolateral, and posteromedial), and the superior colliculus (SC) in awake mice of both sexes using single-unit recordings. Consistent with the feed-forward propagation of adaptation along the visual hierarchy, we find that neurons in layer 4 adapt less strongly than those in other layers of V1. Furthermore, neurons in the HVAs adapt more strongly, and recover more slowly, than those in V1. The magnitude and time course of adaptation was comparable in each of the HVAs and in the SC, suggesting that adaptation may not linearly accumulate along the feed-forward visual processing hierarchy. Despite the increase in adaptation in the HVAs compared with V1, the effects were similarly orientation specific across all areas. These data reveal that adaptation profoundly shapes cortical processing, with increasing impact at higher levels in the cortical hierarchy, and also strongly influencing computations in the SC. Thus, we find robust, brain-wide effects of rapid adaptation on sensory processing.NEW & NOTEWORTHY Rapid adaptation dynamically alters sensory signals to account for recent experience. To understand how adaptation affects sensory processing and perception, we must determine how it impacts the diverse set of cortical and subcortical areas along the hierarchy of the mouse visual system. We find that rapid adaptation strongly impacts neurons in primary visual cortex, the higher visual areas, and the colliculus, consistent with its profound effects on behavior.

Single-unit activity in marmoset posterior parietal cortex in a gap saccade task

Abnormal saccadic eye movements can serve as biomarkers for patients with several neuropsychiatric disorders. The common marmoset ( Callithrix jacchus) is becoming increasingly popular as a nonhuman primate model to investigate the cortical mechanisms of saccadic control. Recently, our group demonstrated that microstimulation in the posterior parietal cortex (PPC) of marmosets elicits contralateral saccades. Here we recorded single-unit activity in the PPC of the same two marmosets using chronic microelectrode arrays while the monkeys performed a saccadic task with gap trials (target onset lagged fixation point offset by 200 ms) interleaved with step trials (fixation point disappeared when the peripheral target appeared). Both marmosets showed a gap effect, shorter saccadic reaction times (SRTs) in gap vs. step trials. On average, stronger gap-period responses across the entire neuronal population preceded shorter SRTs on trials with contralateral targets although this correlation was stronger among the 15% “gap neurons,” which responded significantly during the gap. We also found 39% “target neurons” with significant saccadic target-related responses, which were stronger in gap trials and correlated with the SRTs better than the remaining neurons. Compared with saccades with relatively long SRTs, short-SRT saccades were preceded by both stronger gap-related and target-related responses in all PPC neurons, regardless of whether such response reached significance. Our findings suggest that the PPC in the marmoset contains an area that is involved in the modulation of saccadic preparation.

NEW & NOTEWORTHY As a primate model in systems neuroscience, the marmoset is a great complement to the macaque monkey because of its unique advantages. To identify oculomotor networks in the marmoset, we recorded from the marmoset posterior parietal cortex during a saccadic task and found single-unit activities consistent with a role in saccadic modulation. This finding supports the marmoset as a valuable model for studying oculomotor control.

Neuronal Adaptation Reveals a Suboptimal Decoding of Orientation Tuned Populations in the Mouse Visual Cortex

Sensory information is encoded by populations of cortical neurons. Yet, it is unknown how this information is used for even simple perceptual choices such as discriminating orientation. To determine the computation underlying this perceptual choice, we took advantage of the robust visual adaptation in mouse primary visual cortex (V1). We first designed a stimulus paradigm in which we could vary the degree of neuronal adaptation measured in V1 during an orientation discrimination task. We then determined how adaptation affects task performance for mice of both sexes and tested which neuronal computations are most consistent with the behavioral results given the adapted population responses in V1. Despite increasing the reliability of the population representation of orientation among neurons, and improving the ability of a variety of optimal decoders to discriminate target from distractor orientations, adaptation increases animals' behavioral thresholds. Decoding the animals' choice from neuronal activity revealed that this unexpected effect on behavior could be explained by an overreliance of the perceptual choice circuit on target preferring neurons and a failure to appropriately discount the activity of neurons that prefer the distractor. Consistent with this all-positive computation, we find that animals' task performance is susceptible to subtle perturbations of distractor orientation and optogenetic suppression of neuronal activity in V1. This suggests that to solve this task the circuit has adopted a suboptimal and task-specific computation that discards important task-related information.SIGNIFICANCE STATEMENT A major goal in systems neuroscience is to understand how sensory signals are used to guide behavior. This requires determining what information in sensory cortical areas is used, and how it is combined, by downstream perceptual choice circuits. Here we demonstrate that when performing a go/no-go orientation discrimination task, mice suboptimally integrate signals from orientation tuned visual cortical neurons. While they appropriately positively weight target-preferring neurons, they fail to negatively weight distractor-preferring neurons. We propose that this all-positive computation may be adopted because of its simple learning rules and faster processing, and may be a common approach to perceptual decision-making when task conditions allow.

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.

Laws of concatenated perception: Vision goes for novelty, decisions for perseverance

Every instant of perception depends on a cascade of brain processes calibrated to the history of sensory and decisional events. In the present work, we show that human visual perception is constantly shaped by two contrasting forces exerted by sensory adaptation and past decisions. In a series of experiments, we used multilevel modeling and cross-validation approaches to investigate the impact of previous stimuli and decisions on behavioral reports during adjustment and forced-choice tasks. Our results revealed that each perceptual report is permeated by opposite biases from a hierarchy of serially dependent processes: Low-level adaptation repels perception away from previous stimuli, whereas decisional traces attract perceptual reports toward the recent past. In this hierarchy of serial dependence, "continuity fields" arise from the inertia of decisional templates and not from low-level sensory processes. This finding is consistent with a Two-process model of serial dependence in which the persistence of readout weights in a decision unit compensates for sensory adaptation, leading to attractive biases in sequential perception. We propose a unified account of serial dependence in which functionally distinct mechanisms, operating at different stages, promote the differentiation and integration of visual information over time.

Contrast and luminance adaptation alter neuronal coding and perception of stimulus orientation

Sensory systems face a barrage of stimulation that continually changes along multiple dimensions. These simultaneous changes create a formidable problem for the nervous system, as neurons must dynamically encode each stimulus dimension, despite changes in other dimensions. Here, we measured how neurons in visual cortex encode orientation following changes in luminance and contrast, which are critical for visual processing, but nuisance variables in the context of orientation coding. Using information theoretic analysis and population decoding approaches, we find that orientation discriminability is luminance and contrast dependent, changing over time due to firing rate adaptation. We also show that orientation discrimination in human observers changes during adaptation, in a manner consistent with the neuronal data. Our results suggest that adaptation does not maintain information rates per se, but instead acts to keep sensory systems operating within the limited dynamic range afforded by spiking activity, despite a wide range of possible inputs.

Sensory systems produce stable stimulus representations despite constant changes across multiple stimulus dimensions. Here, the authors reveal dynamic neural coding mechanisms by testing how coding of one dimension (orientation) changes with adaptations to other dimensions (luminance and contrast).

Understanding Sensory Information Processing Through Simultaneous Multi-area Population Recordings

The goal of sensory neuroscience is to understand how the brain creates its myriad of representations of the world, and uses these representations to produce perception and behavior. Circuits of neurons in spatially segregated regions of brain tissue have distinct functional specializations, and these regions are connected to form a functional processing hierarchy. Advances in technology for recording neuronal activity from multiple sites in multiple cortical areas mean that we are now able to collect data that reflects how information is transformed within and between connected members of this hierarchy. This advance is an important step in understanding the brain because, after the sensory organs have transduced a physical signal, every processing stage takes the activity of other neurons as its input, not measurements of the physical world. However, as we explore the potential of studying how populations of neurons in multiple areas respond in concert, we must also expand both the analytical tools that we use to make sense of these data and the scope of the theories that we attempt to define. In this article, we present an overview of some of the most promising analytical approaches for making inferences from population recordings in multiple brain areas, such as dimensionality reduction and measuring changes in correlated variability, and examine how they may be used to address longstanding questions in sensory neuroscience.

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.

Auditory motion does not modulate spiking activity in the middle temporal and medial superior temporal visual areas

The integration of multiple sensory modalities is a key aspect of brain function, allowing animals to take advantage of concurrent sources of information to make more accurate perceptual judgments. For many years, multisensory integration in the cerebral cortex was deemed to occur only in high-level "polysensory" association areas. However, more recent studies have suggested that cross-modal stimulation can also influence neural activity in areas traditionally considered to be unimodal. In particular, several human neuroimaging studies have reported that extrastriate areas involved in visual motion perception are also activated by auditory motion, and may integrate audiovisual motion cues. However, the exact nature and extent of the effects of auditory motion on the visual cortex have not been studied at the single neuron level. We recorded the spiking activity of neurons in the middle temporal (MT) and medial superior temporal (MST) areas of anesthetized marmoset monkeys upon presentation of unimodal stimuli (moving auditory or visual patterns), as well as bimodal stimuli (concurrent audiovisual motion). Despite robust, direction selective responses to visual motion, none of the sampled neurons responded to auditory motion stimuli. Moreover, concurrent moving auditory stimuli had no significant effect on the ability of single MT and MST neurons, or populations of simultaneously recorded neurons, to discriminate the direction of motion of visual stimuli (moving random dot patterns with varying levels of motion noise). Our findings do not support the hypothesis that direct interactions between MT, MST and areas low in the hierarchy of auditory areas underlie audiovisual motion integration.

Neuronal Correlations in MT and MST Impair Population Decoding of Opposite Directions of Random Dot Motion

The study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.

Novel method of extracting motion from natural movies

BACKGROUND

The visual system in primates can be segregated into motion and shape pathways. Interaction occurs at multiple stages along these pathways. Processing of shape-from-motion and biological motion is considered to be a higher-order integration process involving motion and shape information. However, relatively limited types of stimuli have been used in previous studies on these integration processes.

NEW METHOD

We propose a new algorithm to extract object motion information from natural movies and to move random dots in accordance with the information. The object motion information is extracted by estimating the dynamics of local normal vectors of the image intensity projected onto the x-y plane of the movie.

RESULTS

An electrophysiological experiment on two adult common marmoset monkeys (Callithrix jacchus) showed that the natural and random dot movies generated with this new algorithm yielded comparable neural responses in the middle temporal visual area.

COMPARISON WITH EXISTING METHODS

In principle, this algorithm provided random dot motion stimuli containing shape information for arbitrary natural movies. This new method is expected to expand the neurophysiological and psychophysical experimental protocols to elucidate the integration processing of motion and shape information in biological systems.

CONCLUSIONS

The novel algorithm proposed here was effective in extracting object motion information from natural movies and provided new motion stimuli to investigate higher-order motion information processing.

Adaptation in the visual cortex: a case for probing neuronal populations with natural stimuli

The perception of, and neural responses to, sensory stimuli in the present are influenced by what has been observed in the past—a phenomenon known as adaptation. We focus on adaptation in visual cortical neurons as a paradigmatic example. We review recent work that represents two shifts in the way we study adaptation, namely (i) going beyond single neurons to study adaptation in populations of neurons and (ii) going beyond simple stimuli to study adaptation to natural stimuli. We suggest that efforts in these two directions, through a closer integration of experimental and modeling approaches, will enable a more complete understanding of cortical processing in natural environments.

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.

Sensitivity of neurons in the middle temporal area of marmoset monkeys to random dot motion

Neurons in the middle temporal area (MT) of the primate cerebral cortex respond to moving visual stimuli. The sensitivity of MT neurons to motion signals can be characterized by using random-dot stimuli, in which the strength of the motion signal is manipulated by adding different levels of noise (elements that move in random directions). In macaques, this has allowed the calculation of "neurometric" thresholds. We characterized the responses of MT neurons in sufentanil/nitrous oxide-anesthetized marmoset monkeys, a species that has attracted considerable recent interest as an animal model for vision research. We found that MT neurons show a wide range of neurometric thresholds and that the responses of the most sensitive neurons could account for the behavioral performance of macaques and humans. We also investigated factors that contributed to the wide range of observed thresholds. The difference in firing rate between responses to motion in the preferred and null directions was the most effective predictor of neurometric threshold, whereas the direction tuning bandwidth had no correlation with the threshold. We also showed that it is possible to obtain reliable estimates of neurometric thresholds using stimuli that were not highly optimized for each neuron, as is often necessary when recording from large populations of neurons with different receptive field concurrently, as was the case in this study. These results demonstrate that marmoset MT shows an essential physiological similarity to macaque MT and suggest that its neurons are capable of representing motion signals that allow for comparable motion-in-noise judgments.NEW & NOTEWORTHY We report the activity of neurons in marmoset MT in response to random-dot motion stimuli of varying coherence. The information carried by individual MT neurons was comparable to that of the macaque, and the maximum firing rates were a strong predictor of sensitivity. Our study provides key information regarding the neural basis of motion perception in the marmoset, a small primate species that is becoming increasingly popular as an experimental model.

Rapid Sensory Adaptation Redux: A Circuit Perspective

Adaptation is fundamental to life. All organisms adapt over timescales that span from evolution to generations and lifetimes to moment-by-moment interactions. The nervous system is particularly adept at rapidly adapting to change, and this in fact may be one of its fundamental principles of organization and function. Rapid forms of sensory adaptation have been well documented across all sensory modalities in a wide range of organisms, yet we do not have a comprehensive understanding of the adaptive cellular mechanisms that ultimately give rise to the corresponding percepts, due in part to the complexity of the circuitry. In this Perspective, we aim to build links between adaptation at multiple scales of neural circuitry by investigating the differential adaptation across brain regions and sub-regions and across specific cell types, for which the explosion of modern tools has just begun to enable. This investigation points to a set of challenges for the field to link functional observations to adaptive properties of the neural circuit that ultimately underlie percepts.

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.