Natural image statistics mediate brightness 'filling in'

Although the human visual system can accurately estimate the reflectance (or lightness) of surfaces under enormous variations in illumination, two equiluminant grey regions can be induced to appear quite different simply by placing a light-dark luminance transition between them. This illusion, the Craik-Cornsweet-O'Brien (CCOB) effect, has been taken as evidence for a low-level 'filling-in' mechanism subserving lightness perception. Here, we present evidence that the mechanism responsible for the CCOB effect operates not via propagation of a neural signal across space but by amplification of the low spatial frequency (SF) structure of the image. We develop a simple computational model that relies on the statistics of natural scenes actively to reconstruct the image that is most likely to have caused an observed series of responses across SF channels. This principle is tested psychophysically by deriving classification images (CIs) for subjects' discrimination of the contrast polarity of CCOB stimuli masked with noise. CIs resemble 'filled-in' stimuli; i.e. observers rely on portions of the stimuli that contain no information per se but that correspond closely to the reported perceptual completion. As predicted by the model, the filling-in process is contingent on the presence of appropriate low SF structure.     

The role of the posterior parietal cortex in coordinate transformations for visual-motor integration

Lesion to the posterior parietal cortex in monkeys and humans produces spatial deficits in movement and perception. In recording experiments from area 7a, a cortical subdivision in the posterior parietal cortex in monkeys, we have found neurons whose responses are a function of both the retinal location of visual stimuli and the position of the eyes in the orbits. By combining these signals area 7 a neurons code the location of visual stimuli with respect to the head. However, these cells respond over only limited ranges of eye positions (eye-position-dependent coding). To code location in craniotopic space at all eye positions (eye-position-independent coding) an additional step in neural processing is required that uses information distributed across populations of area 7a neurons. We describe here a neural network model, based on back-propagation learning, that both demonstrates how spatial location could be derived from the population response of area 7a neurons and accurately accounts for the observed response properties of these neurons.     

Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas

The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas defined by retinotopic mapping were examined using supervised classifiers applied to responses elicited by a range of stimuli. Using two distinct datasets obtained using wide-field and two-photon imaging, we show that the area labels predicted by the classifiers were highly consistent with the labels obtained using retinotopy. Furthermore, the classifiers were able to model the boundaries of visual areas using resting state cortical responses obtained without any overt stimulus, in both datasets. With the wide-field dataset, clustering neuronal responses using a constrained semi-supervised classifier showed graceful degradation of accuracy. The results suggest that responses from visual cortical areas can be classified effectively using data-driven models. These responses likely reflect unique circuits within each area that give rise to activity with stronger intra-areal than inter-areal correlations, and their responses to controlled visual stimuli across trials drive higher areal classification accuracy than resting state responses.     

Simulation of angiogenesis in three dimensions: Application to cerebral cortex

The vasculature is a dynamic structure, growing and regressing in response to embryonic development, growth, changing physiological demands, wound healing, tumor growth and other stimuli. At the microvascular level, network geometry is not predetermined, but emerges as a result of biological responses of each vessel to the stimuli that it receives. These responses may be summarized as angiogenesis, remodeling and pruning. Previous theoretical simulations have shown how two-dimensional vascular patterns generated by these processes in the mesentery are consistent with experimental observations. During early development of the brain, a mesh-like network of vessels is formed on the surface of the cerebral cortex. This network then forms branches into the cortex, forming a three-dimensional network throughout its thickness. Here, a theoretical model is presented for this process, based on known or hypothesized vascular response mechanisms together with experimentally obtained information on the structure and hemodynamics of the mouse cerebral cortex. According to this model, essential components of the system include sensing of oxygen levels in the midrange of partial pressures and conducted responses in vessel walls that propagate information about metabolic needs of the tissue to upstream segments of the network. The model provides insights into the effects of deficits in vascular response mechanisms, and can be used to generate physiologically realistic microvascular network structures.     

Local non-linear interactions in the visual cortex may reflect global decorrelation

The classical receptive field in the primary visual cortex have been successfully explained by sparse activation of relatively independent units, whose tuning properties reflect the statistical dependencies in the natural environment. Robust surround modulation, emerging from stimulation beyond the classical receptive field, has been associated with increase of lifetime sparseness in the V1, but the system-wide modulation of response strength have currently no theoretical explanation. We measured fMRI responses from human visual cortex and quantified the contextual modulation with a decorrelation coefficient (d), derived from a subtractive normalization model. All active cortical areas demonstrated local non-linear summation of responses, which were in line with hypothesis of global decorrelation of voxels responses. In addition, we found sensitivity to surrounding stimulus structure across the ventral stream, and large-scale sensitivity to the number of simultaneous objects. Response sparseness across voxel population increased consistently with larger stimuli. These data suggest that contextual modulation for a stimulus event reflect optimization of the code and perhaps increase in energy efficiency throughout the ventral stream hierarchy. Our model provides a novel prediction that average suppression of response amplitude for simultaneous stimuli across the cortical network is a monotonic function of similarity of response strengths in the network when the stimuli are presented alone.     

Long-Term Stability of Visual Pattern Selective Responses of Monkey Temporal Lobe Neurons

Many neurons in primate inferotemporal (IT) cortex respond selectively to complex, often meaningful, stimuli such as faces and objects. An important unanswered question is whether such response selectivity, which is thought to arise from experience-dependent plasticity, is maintained from day to day, or whether the roles of individual cells are continually reassigned based on the diet of natural vision. We addressed this question using microwire electrodes that were chronically implanted in the temporal lobe of two monkeys, often allowing us to monitor activity of individual neurons across days. We found that neurons maintained their selectivity in both response magnitude and patterns of spike timing across a large set of visual images throughout periods of stable signal isolation from the same cell that sometimes exceeded two weeks. These results indicate that stimulus-selectivity of responses in IT is stable across days and weeks of visual experience.     

Long-range, pattern-dependent contextual effects in early human visual cortex

The standard view of neurons in early visual cortex is that they behave like localized feature detectors. Here we demonstrate that processing in early visual areas goes beyond feature detection by showing that neural responses are greater when a feature deviates from its context compared to when it does not deviate from its context. Using psychophysics, fMRI, and electroencephalography methodologies, we measured neural responses to an oriented Gabor ("target") embedded in various visual patterns as defined by the relative orientation of flanking stimuli. We first show using psychophysical contrast adaptation and fMRI that a target that differs from its context results in more neural activity compared to a target that is contained within an alternating sequence, suggesting that neurons in early visual cortex are sensitive to large-scale orientation patterns. Next, we use event-related potentials to show that orientation deviations affect the earliest sensory components of the target response. Finally, we use forced-choice classification of "noise" stimuli to show that we are more likely to "see" orientations that deviate from the context. Our results suggest that early visual cortex is sensitive to global patterns in images in a way that is markedly different from the predictions of standard models of cortical visual processing.     

Timing Precision in Population Coding of Natural Scenes in the Early Visual System

The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ~10-ms resolution is a crucial property of the neural code entering cortex.     

Timescales of multineuronal activity patterns reflect temporal structure of visual stimuli

The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5-20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales.     

Opponent inhibition: a developmental model of layer 4 of the neocortical circuit.

We model the development of the functional circuit of layer 4 (the input-recipient layer) of cat primary visual cortex. The observed thalamocortical and intracortical circuitry codevelop under Hebb-like synaptic plasticity. Hebbian development yields opponent inhibition: inhibition evoked by stimuli anticorrelated with those that excite a cell. Strong opponent inhibition enables recognition of stimulus orientation in a manner invariant to stimulus contrast. These principles may apply to cortex more generally: Hebb-like plasticity can guide layer 4 of any piece of cortex to create opposition between anticorrelated stimulus pairs, and this enables recognition of specific stimulus patterns in a manner invariant to stimulus magnitude. Properties that are invariant across a cortical column are predicted to be those shared by opponent stimulus pairs; this contrasts with the common idea that a column represents cells with similar response properties.     

Pheromones enhance somatosensory processing in newt brains through a vasotocin-dependent mechanism

We tested whether the sex pheromones that stimulate courtship clasping in male roughskin newts do so, at least in part, by amplifying the somatosensory signals that directly trigger the motor pattern associated with clasping and, if so, whether that amplification is dependent on endogenous vasotocin (VT). Female olfactory stimuli increased the number of action potentials recorded in the medulla of males in response to tactile stimulation of the cloaca, which triggers the clasp motor reflex, as well as to tactile stimulation of the snout and hindlimb. That enhancement was blocked by exposing the medulla to a V1a receptor antagonist before pheromone exposure. However, the antagonist did not affect medullary responses to tactile stimuli in the absence of pheromone exposure, suggesting that pheromones amplify somatosensory signals by inducing endogenous VT release. The ability of VT to couple sensory systems together in response to social stimulation could allow this peptide to induce variable behavioural outcomes, depending on the immediate context of the social interaction and thus on the nature of the associated stimuli that are amplified. If widespread in vertebrates, this mechanism could account for some of the behavioural variability associated with this and related peptides both within and across species.     

Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation

Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.     

Differential responses of normal human coronary artery endothelial cells against multiple cytokines comparatively assessed by gene expression profiles

Endothelial cells play an important role in terms of biological functions by responding to a variety of stimuli in the blood. However, little is known about the molecular mechanism involved in rendering the variety in the cellular response. To investigate the variety of the cellular responses against exogenous stimuli at the gene expression level, we attempted to describe the cellular responses with comprehensive gene expression profiles, dissect them into multiple response patterns, and characterize the response patterns according to the information accumulated so far on the genes included in the patterns. We comparatively analyzed in parallel the gene expression profiles obtained with DNA microarrays from normal human coronary artery endothelial cells (HCAECs) stimulated with multiple cytokines, interleukin-1β, tumor necrosis factor-, interferon-β, interferon-γ, and oncostatin M, which are profoundly involved in various functional responses of endothelial cells. These analyses revealed that the cellular responses of HCAECs against these cytokines included at least 15 response patterns specific to a single cytokine or common to multiple cytokines. Moreover, we statistically extracted genes contained within the individual response patterns and characterized the response patterns with the genes referring to the previously accumulated findings including the biological process defined by the Gene Ontology Consortium (GO). Out of the 15 response patterns in which at least one gene was successfully extracted through the statistical approach, 11 response patterns were differentially characterized by representing the number of genes contained in individual criteria of the biological process in the GO only. The approach to dissect cellular responses into response patterns and to characterize the pattern at the gene expression level may contribute to the gaining of insight for untangling the diversity of cellular functions.     

A Normalization Model of Attentional Modulation of Single Unit Responses

Although many studies have shown that attention to a stimulus can enhance the responses of individual cortical sensory neurons, little is known about how attention accomplishes this change in response. Here, we propose that attention-based changes in neuronal responses depend on the same response normalization mechanism that adjusts sensory responses whenever multiple stimuli are present. We have implemented a model of attention that assumes that attention works only through this normalization mechanism, and show that it can replicate key effects of attention. The model successfully explains how attention changes the gain of responses to individual stimuli and also why modulation by attention is more robust and not a simple gain change when multiple stimuli are present inside a neuron''s receptive field. Additionally, the model accounts well for physiological data that measure separately attentional modulation and sensory normalization of the responses of individual neurons in area MT in visual cortex. The proposal that attention works through a normalization mechanism sheds new light a broad range of observations on how attention alters the representation of sensory information in cerebral cortex.     

Sparse representation of sounds in the unanesthetized auditory cortex

How do neuronal populations in the auditory cortex represent acoustic stimuli? Although sound-evoked neural responses in the anesthetized auditory cortex are mainly transient, recent experiments in the unanesthetized preparation have emphasized subpopulations with other response properties. To quantify the relative contributions of these different subpopulations in the awake preparation, we have estimated the representation of sounds across the neuronal population using a representative ensemble of stimuli. We used cell-attached recording with a glass electrode, a method for which single-unit isolation does not depend on neuronal activity, to quantify the fraction of neurons engaged by acoustic stimuli (tones, frequency modulated sweeps, white-noise bursts, and natural stimuli) in the primary auditory cortex of awake head-fixed rats. We find that the population response is sparse, with stimuli typically eliciting high firing rates (>20 spikes/second) in less than 5% of neurons at any instant. Some neurons had very low spontaneous firing rates (<0.01 spikes/second). At the other extreme, some neurons had driven rates in excess of 50 spikes/second. Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported. Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex. Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.     

The Role of Thalamic Population Synchrony in the Emergence of Cortical Feature Selectivity

In a wide range of studies, the emergence of orientation selectivity in primary visual cortex has been attributed to a complex interaction between feed-forward thalamic input and inhibitory mechanisms at the level of cortex. Although it is well known that layer 4 cortical neurons are highly sensitive to the timing of thalamic inputs, the role of the stimulus-driven timing of thalamic inputs in cortical orientation selectivity is not well understood. Here we show that the synchronization of thalamic firing contributes directly to the orientation tuned responses of primary visual cortex in a way that optimizes the stimulus information per cortical spike. From the recorded responses of geniculate X-cells in the anesthetized cat, we synthesized thalamic sub-populations that would likely serve as the synaptic input to a common layer 4 cortical neuron based on anatomical constraints. We used this synchronized input as the driving input to an integrate-and-fire model of cortical responses and demonstrated that the tuning properties match closely to those measured in primary visual cortex. By modulating the overall level of synchronization at the preferred orientation, we show that efficiency of information transmission in the cortex is maximized for levels of synchronization which match those reported in thalamic recordings in response to naturalistic stimuli, a property which is relatively invariant to the orientation tuning width. These findings indicate evidence for a more prominent role of the feed-forward thalamic input in cortical feature selectivity based on thalamic synchronization.     

Auditory topography and temporal response dynamics of canary caudal telencephalon

To map the encoding of auditory cues in songbirds, multiunit electrophysiological responses to pure tone stimuli (250-5000 Hz) were recorded at 373 sites throughout the avian analogue of the mammalian auditory cortex in the caudal telencephalon of awake, restrained canaries. We found that a dorso-ventral tonotopic gradient from low to high frequency stimuli extends from the rostral field L2 to caudal-most caudo-medial nidopallium (NCM), similar to the frequency-dependent patterns of ZENK gene expression in canary NCM and to electrophysiological responses in other songbird species. However, response characteristics differ across the region. In field L2, responses are vigorous, phasic, and do not habituate to repeated presentations of the same stimulus. In an important subset of field L2 sites, tuning width narrows over the course of the response, which then terminates rapidly at stimulus offset. These properties are associated with inhibition at many nonpreferred frequencies and poststimulus inhibition at responsive frequencies. In contrast, NCM sites habituate to repeated sine waves, have wider tuning and lower amplitude responses, and rarely show inhibitory effects. Tuning curves in NCM are also flatter than those of field L2, and are often multipeaked. In addition, tuning width increases as the response unfolds and poststimulus excitation is often sustained in NCM. In sum, specific parts of the canary caudo-medial telencephalon differ in their response properties, suggesting differential roles in auditory processing. NCM properties, in particular, may allow for response integration across multiple spectrally varying stimulus elements, such as those that occur during birdsong.     

Gender-Specificity of Initial and Controlled Visual Attention to Sexual Stimuli in Androphilic Women and Gynephilic Men

Research across groups and methods consistently finds a gender difference in patterns of specificity of genital response; however, empirically supported mechanisms to explain this difference are lacking. The information-processing model of sexual arousal posits that automatic and controlled cognitive processes are requisite for the generation of sexual responses. Androphilic women’s gender-nonspecific response patterns may be the result of sexually-relevant cues that are common to both preferred and nonpreferred genders capturing attention and initiating an automatic sexual response, whereas men’s attentional system may be biased towards the detection and response to sexually-preferred cues only. In the present study, we used eye tracking to assess visual attention to sexually-preferred and nonpreferred cues in a sample of androphilic women and gynephilic men. Results support predictions from the information-processing model regarding gendered processing of sexual stimuli in men and women. Men’s initial attention patterns were gender-specific, whereas women’s were nonspecific. In contrast, both men and women exhibited gender-specific patterns of controlled attention, although this effect was stronger among men. Finally, measures of attention and self-reported attraction were positively related in both men and women. These findings are discussed in the context of the information-processing model and evolutionary mechanisms that may have evolved to promote gendered attentional systems.