Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

A neural model of modified excitation/inhibition and feedback levels in schizophrenia

Introduction

The strength of certain visual illusions, including contrast-contrast and apparent motion, is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms.

Methods

To investigate whether and how these factors influence the perceived contrast-contrast and apparent motion illusions in individuals with schizophrenia, we propose a two-layer network, with top-down feedback from layer 2 to layer 1 that can model visual receptive fields (RFs) and their inhibitory and excitatory subfields.

Results

Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia within certain clinical states and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups.

Discussion

We propose that the heterogeneity of visual and in general sensory processing in certain clinical states of schizophrenia can be largely explained by the degree of top-down feedback reduction, emphasizing the critical role of top-down feedback in illusion perception, and to a lesser extent on the imbalance of excitation/inhibition. Our neural model provides a mechanistic explanation for the modulated visual percepts of contrast-contrast and apparent motion in schizophrenia with findings that can explain a broad range of visual perceptual observations in previous studies. The two-layer motif of the current model provides a general framework that can be tailored to investigate subcortico-cortical (such as thalamocortical) and cortico-cortical networks, bridging neurobiological changes in schizophrenia and perceptual processing.

A neural model of modified excitation/inhibition and feedback levels in schizophrenia

The strength of certain visual illusions is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms. To investigate whether and how these factors influence the perceived illusions in individuals with schizophrenia, we propose a two-layer network that can model visual receptive fields (RFs), their inhibitory and excitatory subfields, and the top-down feedback. Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups. We propose that the heterogeneity of visual and in general sensory processing in schizophrenia can be largely explained by the degree of top-down feedback reduction, emphasizing the critical role of top-down feedback in illusion perception, and to a lesser extent on the imbalance of excitation/inhibition. Our neural model provides a mechanistic explanation for the modulated visual percepts in schizophrenia with findings that can explain a broad range of visual perceptual observations in previous studies. The two-layer motif of the current model provides a general framework that can be tailored to investigate subcortico-cortical (such as thalamocortical) and cortico-cortical networks, bridging neurobiological changes in schizophrenia and perceptual processing.

Hierarchical Organization of Corticothalamic Projections to the Pulvinar

Signals from lower cortical visual areas travel to higher-order areas for further processing through cortico-cortical projections, organized in a hierarchical manner. These signals can also be transferred between cortical areas via alternative cortical transthalamic routes involving higher-order thalamic nuclei like the pulvinar. It is unknown whether the organization of transthalamic pathways may reflect the cortical hierarchy. Two axon terminal types have been identified in corticothalamic (CT) pathways: the types I (modulators) and II (drivers) characterized by thin axons with small terminals and by thick axons and large terminals, respectively. In cats, projections from V1 to the pulvinar complex comprise mainly type II terminals, whereas those from extrastriate areas include a combination of both terminals suggesting that the nature of CT terminals varies with the hierarchical order of visual areas. To test this hypothesis, distribution of CT terminals from area 21a was charted and compared with 3 other visual areas located at different hierarchical levels. Results demonstrate that the proportion of modulatory CT inputs increases along the hierarchical level of cortical areas. This organization of transthalamic pathways reflecting cortical hierarchy provides new and fundamental insights for the establishment of more accurate models of cortical signal processing along transthalamic cortical pathways.

Deep Neural Networks for Modeling Visual Perceptual Learning

Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. Although existing models aid our knowledge of critical aspects of VPL, the connections shown by these models between behavioral learning and plasticity across different brain areas are typically superficial. Most models explain VPL as readout from simple perceptual representations to decision areas and are not easily adaptable to explain new findings. Here, we show that a well -known instance of deep neural network (DNN), whereas not designed specifically for VPL, provides a computational model of VPL with enough complexity to be studied at many levels of analyses. After learning a Gabor orientation discrimination task, the DNN model reproduced key behavioral results, including increasing specificity with higher task precision, and also suggested that learning precise discriminations could transfer asymmetrically to coarse discriminations when the stimulus conditions varied. Consistent with the behavioral findings, the distribution of plasticity moved toward lower layers when task precision increased and this distribution was also modulated by tasks with different stimulus types. Furthermore, learning in the network units demonstrated close resemblance to extant electrophysiological recordings in monkey visual areas. Altogether, the DNN fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. Although the comparisons were mostly qualitative, the DNN provides a new method of studying VPL, can serve as a test bed for theories, and assists in generating predictions for physiological investigations.SIGNIFICANCE STATEMENT Visual perceptual learning (VPL) has been found to cause changes at multiple stages of the visual hierarchy. We found that training a deep neural network (DNN) on an orientation discrimination task produced behavioral and physiological patterns similar to those found in human and monkey experiments. Unlike existing VPL models, the DNN was pre-trained on natural images to reach high performance in object recognition, but was not designed specifically for VPL; however, it fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. When used with care, this unbiased and deep-hierarchical model can provide new ways of studying VPL from behavior to physiology.

Revealing Detail along the Visual Hierarchy: Neural Clustering Preserves Acuity from V1 to V4

How primates perceive objects along with their detailed features remains a mystery. This ability to make fine visual discriminations depends upon a high-acuity analysis of spatial frequency (SF) along the visual hierarchy from V1 to inferotemporal cortex. By studying the transformation of SF across macaque parafoveal V1, V2, and V4, we discovered SF-selective functional domains in V4 encoding higher SFs up to 12 cycles/°. These intermittent higher-SF-selective domains, surrounded by domains encoding lower SFs, violate the inverse relationship between SF preference and retinal eccentricity. The neural activities of higher- and lower-SF domains correspond to local and global features, respectively, of the same stimuli. Neural response latencies in high-SF domains are around 10 ms later than in low-SF domains, consistent with the coarse-to-fine nature of perception. Thus, our finding of preserved resolution from V1 into V4, separated both spatially and temporally, may serve as a connecting link for detailed object representation.

Compressive Temporal Summation in Human Visual Cortex

Combining sensory inputs over space and time is fundamental to vision. Population receptive field models have been successful in characterizing spatial encoding throughout the human visual pathways. A parallel question, how visual areas in the human brain process information distributed over time, has received less attention. One challenge is that the most widely used neuroimaging method, fMRI, has coarse temporal resolution compared with the time-scale of neural dynamics. Here, via carefully controlled temporally modulated stimuli, we show that information about temporal processing can be readily derived from fMRI signal amplitudes in male and female subjects. We find that all visual areas exhibit subadditive summation, whereby responses to longer stimuli are less than the linear prediction from briefer stimuli. We also find fMRI evidence that the neural response to two stimuli is reduced for brief interstimulus intervals (indicating adaptation). These effects are more pronounced in visual areas anterior to V1-V3. Finally, we develop a general model that shows how these effects can be captured with two simple operations: temporal summation followed by a compressive nonlinearity. This model operates for arbitrary temporal stimulation patterns and provides a simple and interpretable set of computations that can be used to characterize neural response properties across the visual hierarchy. Importantly, compressive temporal summation directly parallels earlier findings of compressive spatial summation in visual cortex describing responses to stimuli distributed across space. This indicates that, for space and time, cortex uses a similar processing strategy to achieve higher-level and increasingly invariant representations of the visual world.SIGNIFICANCE STATEMENT Combining sensory inputs over time is fundamental to seeing. Two important temporal phenomena are summation, the accumulation of sensory inputs over time, and adaptation, a response reduction for repeated or sustained stimuli. We investigated these phenomena in the human visual system using fMRI. We built predictive models that operate on arbitrary temporal patterns of stimulation using two simple computations: temporal summation followed by a compressive nonlinearity. Our new temporal compressive summation model captures (1) subadditive temporal summation, and (2) adaptation. We show that the model accounts for systematic differences in these phenomena across visual areas. Finally, we show that for space and time, the visual system uses a similar strategy to achieve increasingly invariant representations of the visual world.

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

Credibility judgments in web page design - a brief review

Today, more than ever, knowledge that interfaces appearance analysis is a crucial point in human-computer interaction field has been accepted. As nowadays virtually anyone can publish information on the web, the credibility role has grown increasingly important in relation to the web-based content. Areas like trust, credibility, and behavior, doubled by overall impression and user expectation are today in the spotlight of research compared to the last period, when other pragmatic areas such as usability and utility were considered. Credibility has been discussed as a theoretical construct in the field of communication in the past decades and revealed that people tend to evaluate the credibility of communication primarily by the communicator's expertise. Other factors involved in the content communication process are trustworthiness and dynamism as well as various other criteria but to a lower extent. In this brief review, factors like web page aesthetics, browsing experiences and user experience are considered.

Differential magnetic resonance neurofeedback modulations across extrinsic (visual) and intrinsic (default-mode) nodes of the human cortex

Previous advances in magnetic resonance imaging allow the analysis of blood oxygen level-dependent signals in real time, thus opening the possibility of feeding an index of these signals back to scanned human participants. However, it is still not known to what extent different cortical networks may differ in their sensitivity to such internally generated neurofeedback (NF). Here, we compare NF efficacy across six cortical regions including: early and high-order visual areas and the posterior parietal lobe, a prominent node of the default mode network (DMN). Our results reveal a consistent difference in NF activation across these areas. Sham controls ruled out a role of attention/arousal in these effects. These differences are suggestive of a relationship to the relative reliance on intrinsic information, moving from early visual cortex (lowest) to the DMN (highest). Interestingly, the visual parahippocampal place area showed NF activation closer to the DMN node. The results are compatible with the notion of the DMN as an intrinsically oriented system.

Delayed signal transmission in area 17, area 18 and the posteromedial lateral suprasylvian area of aged cats

To investigate the effect of senescence on signal transmission, we have compared the visual response latency and spontaneous activity of cells in the lateral geniculate nucleus (LGN), area 17, area 18 and posteromedial lateral suprasylvian area (PMLS) of young and old cats. We found that LGN cells in old cats exhibit largely normal visual response latency. In contrast, all the other three areas exhibited significant aging-related delays in the visual response latency. On average, PMLS showed most pronounced delays among these three areas. Area 18 slowed more than area 17, but this was not significant. The degradation of signal timing in the visual cortex might provide insight into neuronal response mechanism underlying perception slowing during aging.

Fluctuations of visual awareness: combining motion-induced blindness with binocular rivalry

Binocular rivalry (BR) and motion-induced blindness (MIB) are two phenomena of visual awareness where perception alternates between multiple states despite constant retinal input. Both phenomena have been extensively studied, but the underlying processing remains unclear. It has been suggested that BR and MIB involve the same neural mechanism, but how the two phenomena compete for visual awareness in the same stimulus has not been systematically investigated. Here we introduce BR in a dichoptic stimulus display that can also elicit MIB and examine fluctuations of visual awareness over the course of each trial. Exploiting this paradigm we manipulated stimulus characteristics that are known to influence MIB and BR. In two experiments we found that effects on multistable percepts were incompatible with the idea of a common oscillator. The results suggest instead that local and global stimulus attributes can affect the dynamics of each percept differently. We conclude that the two phenomena of visual awareness share basic temporal characteristics but are most likely influenced by processing at different stages within the visual system.

The role of pulvinar in the transmission of information in the visual hierarchy

Visual receptive field (RF) attributes in visual cortex of primates have been explained mainly from cortical connections: visual RFs progress from simple to complex through cortico-cortical pathways from lower to higher levels in the visual hierarchy. This feedforward flow of information is paired with top-down processes through the feedback pathway. Although the hierarchical organization explains the spatial properties of RFs, is unclear how a non-linear transmission of activity through the visual hierarchy can yield smooth contrast response functions in all level of the hierarchy. Depending on the gain, non-linear transfer functions create either a bimodal response to contrast, or no contrast dependence of the response in the highest level of the hierarchy. One possible mechanism to regulate this transmission of visual contrast information from low to high level involves an external component that shortcuts the flow of information through the hierarchy. A candidate for this shortcut is the Pulvinar nucleus of the thalamus. To investigate representation of stimulus contrast a hierarchical model network of ten cortical areas is examined. In each level of the network, the activity from the previous layer is integrated and then non-linearly transmitted to the next level. The arrangement of interactions creates a gradient from simple to complex RFs of increasing size as one moves from lower to higher cortical levels. The visual input is modeled as a Gaussian random input, whose width codes for the contrast. This input is applied to the first area. The output activity ratio among different contrast values is analyzed for the last level to observe sensitivity to a contrast and contrast invariant tuning. For a purely cortical system, the output of the last area can be approximately contrast invariant, but the sensitivity to contrast is poor. To account for an alternative visual processing pathway, non-reciprocal connections from and to a parallel pulvinar like structure of nine areas is coupled to the system. Compared to the pure feedforward model, cortico-pulvino-cortical output presents much more sensitivity to contrast and has a similar level of contrast invariance of the tuning.