Adaptation without Plasticity

Stronger tilt aftereffects in individuals diagnosed with schizophrenia spectrum disorders but not bipolar disorder

An altered use of context and experience to interpret incoming information has been posited to explain schizophrenia symptoms. The visual system can serve as a model system for examining how context and experience guide perception and the neural mechanisms underlying putative alterations. The influence of prior experience on current perception is evident in visual aftereffects, the perception of the "opposite" of a previously viewed stimulus. Aftereffects are associated with neural adaptation and concomitant change in strength of lateral inhibitory connections in visually responsive neurons. In a previous study, we observed stronger aftereffects related to orientation (tilt aftereffects) but not luminance (negative afterimages) in individuals diagnosed with schizophrenia, which we interpreted as potentially suggesting altered cortical (but not subcortical) adaptability and local changes in excitatory-inhibitory interactions. Here, we tested whether stronger tilt aftereffects were specific to individuals with schizophrenia or extended to individuals with bipolar disorder. We measured tilt aftereffects and negative afterimages in 32 individuals with bipolar disorder, and compared aftereffect strength to a previously reported group of 36 individuals with schizophrenia and 22 healthy controls. We observed stronger tilt aftereffects, but not negative afterimages, in individuals with schizophrenia as compared to both controls and individuals with bipolar disorder, who did not differ from each other. These results mitigate concerns that stronger tilt aftereffects in schizophrenia are a consequence of medication or of the psychosocial consequences of a severe mental illness.

Cortical recurrence supports resilience to sensory variance in the primary visual cortex

Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to make decisions. By processing myriads of parallel sensory inputs, our brain is theoretically able to compute the variance of its environment, a cue known to guide our behavior. Yet, the neurobiological and computational basis of such variance computations are still poorly understood. Here, we quantify the dynamics of sensory variance modulations of cat primary visual cortex neurons. We report two archetypal neuronal responses, one of which is resilient to changes in variance and co-encodes the sensory feature and its variance, improving the population encoding of orientation. The existence of these variance-specific responses can be accounted for by a model of intracortical recurrent connectivity. We thus propose that local recurrent circuits process uncertainty as a generic computation, advancing our understanding of how the brain handles naturalistic inputs.

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.

Deep neural models for color classification and color constancy

Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance with varying cues. Inputs to the networks consisted of two-dimensional images of simulated cone excitations derived from three-dimensional (3D) rendered scenes of 2,115 different 3D shapes, with spectral reflectances of 1,600 different Munsell chips, illuminated under 278 different natural illuminations. The models were trained to classify the reflectance of the objects. Testing was done with four new illuminations with equally spaced CIEL*a*b* chromaticities, two along the daylight locus and two orthogonal to it. High levels of color constancy were achieved with different deep neural networks, and constancy was higher along the daylight locus. When gradually removing cues from the scene, constancy decreased. Both ResNets and classical ConvNets of varying degrees of complexity performed well. However, DeepCC, our simplest sequential convolutional network, represented colors along the three color dimensions of human color vision, while ResNets showed a more complex representation.

N-methyl d-aspartate receptor hypofunction reduces visual contextual integration

Visual cognition is finely tuned to the elements in a scene but also relies on contextual integration to improve visual detection and discrimination. This integration is impaired in patients with schizophrenia. Studying impairments in contextual integration may lead to biomarkers of schizophrenia, tools to monitor disease progression, and, in animal models, insight into the underlying neural deficits. We developed a nonhuman primate model to test the hypothesis that hypofunction of the N-methyl d-aspartate receptor (NMDAR) impairs contextual integration. Two male rhesus macaques (Macaca mulatta) were trained to indicate which of two patterns on the screen had the highest contrast. One of these patterns appeared in isolation, and the other was surrounded by a high-contrast pattern. In humans, this high-contrast context is known to lead to an underestimation of contrast. This so-called Chubb illusion is thought to result from surround suppression, a key contextual integration mechanism. To test the involvement of NMDAR in this process, we compared animals' perceptual bias with and without intramuscular injections of a subanesthetic dose of the NMDAR antagonist ketamine. In the absence of ketamine, the animals reported a Chubb illusion - matching reports in healthy humans. Hence, monkeys - just like humans - perform visual contextual integration. This reaffirms the importance of nonhuman primates to help understand visual cognition. Injection of ketamine significantly reduced the strength of the illusion and thus impaired contextual integration. This supports the hypothesis that NMDAR hypofunction plays a causal role in specific behavioral impairments observed in schizophrenia.

Stronger tilt aftereffects in persons with schizophrenia

Individuals with schizophrenia may fail to appropriately use temporal context and apply past environmental regularities to the interpretation of incoming sensory information. Here we use the visual system as a test bed for investigating how prior experience shapes perception in individuals with schizophrenia. Specifically, we use visual aftereffects, illusory percepts resulting from prior exposure to visual input, to measure the influence of prior events on current processing. At a neural level, visual aftereffects arise due to attenuation in the responses of neurons that code the features of the prior stimulus (neuronal adaptation) and subsequent disinhibition of neurons signaling activity at the opposite end of the feature dimension. In the current study, we measured tilt aftereffects and negative afterimages, 2 types of aftereffects that reflect, respectively, adaptation of cortical orientation-coding neurons and adaptation of subcortical and retinal luminance-coding cells in persons with schizophrenia (PSZ; n = 36) and demographically matched healthy controls (HC; n = 22). We observed stronger tilt aftereffects in PSZ compared to HC, but no difference in negative afterimages. Stronger tilt aftereffects were related to more severe negative symptoms. These data suggest oversensitivity to recent regularities, in the form of stronger visual adaptation, at cortical, but not subcortical, levels in schizophrenia. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception

Adaptation is a fundamental property of sensory systems that can change subjective experiences in the context of recent information. Adaptation has been postulated to arise from recurrent circuit mechanisms or as a consequence of neuronally intrinsic suppression. However, it is unclear whether intrinsic suppression by itself can account for effects beyond reduced responses. Here, we test the hypothesis that complex adaptation phenomena can emerge from intrinsic suppression cascading through a feedforward model of visual processing. A deep convolutional neural network with intrinsic suppression captured neural signatures of adaptation including novelty detection, enhancement, and tuning curve shifts, while producing aftereffects consistent with human perception. When adaptation was trained in a task where repeated input affects recognition performance, an intrinsic mechanism generalized better than a recurrent neural network. Our results demonstrate that feedforward propagation of intrinsic suppression changes the functional state of the network, reproducing key neurophysiological and perceptual properties of adaptation.

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.

Short-Term Attractive Tilt Aftereffects Predicted by a Recurrent Network Model of Primary Visual Cortex

Adaptation is a multi-faceted phenomenon that is of interest in terms of both its function and its potential to reveal underlying neural processing. Many behavioral studies have shown that after exposure to an oriented adapter the perceived orientation of a subsequent test is repulsed away from the orientation of the adapter. This is the well-known Tilt Aftereffect (TAE). Recently, we showed that the dynamics of recurrently connected networks may contribute substantially to the neural changes induced by adaptation, especially on short time scales. Here we extended the network model and made the novel behavioral prediction that the TAE should be attractive, not repulsive, on a time scale of a few 100 ms. Our experiments, using a novel adaptation protocol that specifically targeted adaptation on a short time scale, confirmed this prediction. These results support our hypothesis that recurrent network dynamics may contribute to short-term adaptation. More broadly, they show that understanding the neural processing of visual inputs that change on the time scale of a typical fixation requires a detailed analysis of not only the intrinsic properties of neurons, but also the slow and complex dynamics that emerge from their recurrent connectivity. We argue that this is but one example of how even simple recurrent networks can underlie surprisingly complex information processing, and are involved in rudimentary forms of memory, spatio-temporal integration, and signal amplification.

Plasticity and Adaptation in Adult Binocular Vision

Understanding the relationship between changes in sensory perception and functional/structural changes in the brain is a major endeavor in the field of systems neuroscience. Progress in this area holds the potential to reveal how the brain adapts to the demands of a complex and changing environment, as well as to assist with the development of therapeutic interventions to reverse the negative effects of abnormal experience. The cells and circuits that make up the mammalian visual system provide a unique scientific test-bed for studying brain plasticity, thanks to the rich literature on their basic organization and similarity across a range of species. In this minireview, we highlight recent advances in the study of plasticity in adult binocular vision, emphasizing the importance of considering changes that occur over different timescales. We discuss key new insights, significant open questions, and how this research is leading to a broader understanding of the ways that the adult brain maintains a robust ability for adaptation and change.

V1 microcircuit dynamics: altered signal propagation suggests intracortical origins for adaptation in response to visual repetition

Repetitive visual stimulation profoundly changes sensory processing in the primary visual cortex (V1). We show how the associated adaptive changes are linked to an altered flow of synaptic activation across the V1 laminar microcircuit. Using repeated visual stimulation, we recorded layer-specific responses in V1 of two fixating monkeys. We found that repetition-related spiking suppression was most pronounced outside granular V1 layers that receive the main retinogeniculate input. This repetition-related response suppression was robust to alternating stimuli between the eyes, in line with the notion that repetition-related adaptation is predominantly of cortical origin. Most importantly, current source density (CSD) analysis, which provides an estimate of local net depolarization, revealed that synaptic processing during repeated stimulation was most profoundly affected within supragranular layers, which harbor the bulk of cortico-cortical connections. Direct comparison of the temporal evolution of laminar CSD and spiking activity showed that stimulus repetition first affected supragranular synaptic currents, which translated into a reduction of stimulus-evoked spiking across layers. Together, these results suggest that repetition induces an altered state of intracortical processing that underpins visual adaptation. NEW & NOTEWORTHY Our survival depends on our brains rapidly adapting to ever changing environments. A well-studied form of adaptation occurs whenever we encounter the same or similar stimuli repeatedly. We show that this repetition-related adaptation is supported by systematic changes in the flow of sensory activation across the laminar cortical microcircuitry of primary visual cortex. These results demonstrate how adaptation impacts neuronal interactions across cortical circuits.

Short-term visual deprivation boosts the flexibility of body representation

Short-term visual deprivation by blindfolding influences tactile acuity and orientation in space and, on a neural level, leads to enhanced excitability of visual and motor cortices. However, to the best of our knowledge, the possible effects of short-term visual deprivation on body representation have not been examined. In the present study, we tested two groups of 30 healthy participants with the somatic rubber hand illusion, a well-established paradigm to probe the dynamic plasticity of body representation. Before the start of the procedure, the experimental group was blindfolded for 120 minutes, while the control group wore transparent goggles for the same amount of time. We found that although there was no difference in the subjective feeling of ownership of the rubber hand during the illusion, the blindfolded group showed a significantly larger recalibration of hand position sense towards the location of the rubber hand than the control group. This finding suggests that short-term visual deprivation boosts plasticity of body representation in terms of multisensory spatial recalibration of hand position sense.

Recurrent network dynamics reconciles visual motion segmentation and integration

In sensory systems, a range of computational rules are presumed to be implemented by neuronal subpopulations with different tuning functions. For instance, in primate cortical area MT, different classes of direction-selective cells have been identified and related either to motion integration, segmentation or transparency. Still, how such different tuning properties are constructed is unclear. The dominant theoretical viewpoint based on a linear-nonlinear feed-forward cascade does not account for their complex temporal dynamics and their versatility when facing different input statistics. Here, we demonstrate that a recurrent network model of visual motion processing can reconcile these different properties. Using a ring network, we show how excitatory and inhibitory interactions can implement different computational rules such as vector averaging, winner-take-all or superposition. The model also captures ordered temporal transitions between these behaviors. In particular, depending on the inhibition regime the network can switch from motion integration to segmentation, thus being able to compute either a single pattern motion or to superpose multiple inputs as in motion transparency. We thus demonstrate that recurrent architectures can adaptively give rise to different cortical computational regimes depending upon the input statistics, from sensory flow integration to segmentation.

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.

Recurrent Network Dynamics; a Link between Form and Motion

To discriminate visual features such as corners and contours, the brain must be sensitive to spatial correlations between multiple points in an image. Consistent with this, macaque V2 neurons respond selectively to patterns with well-defined multipoint correlations. Here, we show that a standard feedforward model (a cascade of linear–non-linear filters) does not capture this multipoint selectivity. As an alternative, we developed an artificial neural network model with two hierarchical stages of processing and locally recurrent connectivity. This model faithfully reproduced neurons’ selectivity for multipoint correlations. By probing the model, we gained novel insights into early form processing. First, the diverse selectivity for multipoint correlations and complex response dynamics of the hidden units in the model were surprisingly similar to those observed in V1 and V2. This suggests that both transient and sustained response dynamics may be a vital part of form computations. Second, the model self-organized units with speed and direction selectivity that was correlated with selectivity for multipoint correlations. In other words, the model units that detected multipoint spatial correlations also detected space-time correlations. This leads to the novel hypothesis that higher-order spatial correlations could be computed by the rapid, sequential assessment and comparison of multiple low-order correlations within the receptive field. This computation links spatial and temporal processing and leads to the testable prediction that the analysis of complex form and motion are closely intertwined in early visual cortex.