A model of non-linear interactions between cortical top-down and horizontal connections explains the attentional gating of collinear facilitation

Cortical Synchrony as a Mechanism of Collinear Facilitation and Suppression in Early Visual Cortex

Stimulus-induced oscillations and synchrony among neuronal populations in visual cortex are well-established phenomena. Their functional role in cognition are, however, not well-understood. Recent studies have suggested that neural synchrony may underlie perceptual grouping as stimulus-frequency relationships and stimulus-dependent lateral connectivity profiles can determine the success or failure of synchronization among neuronal groups encoding different stimulus elements. We suggest that the same mechanism accounts for collinear facilitation and suppression effects where the detectability of a target Gabor stimulus is improved or diminished by the presence of collinear flanking Gabor stimuli. We propose a model of oscillators which represent three neuronal populations in visual cortex with distinct receptive fields reflecting the target and two flankers, respectively, and whose connectivity is determined by the collinearity of the presented Gabor stimuli. Our model simulations confirm that neuronal synchrony can indeed explain known collinear facilitation and suppression effects for attended and unattended stimuli.

Learning Nonclassical Receptive Field Modulation for Contour Detection

This work develops a biologically inspired neural network for contour detection in natural images by combining the nonclassical receptive field modulation mechanism with a deep learning framework. The input image is first convolved with the local feature detectors to produce the classical receptive field responses, and then a corresponding modulatory kernel is constructed for each feature map to model the nonclassical receptive field modulation behaviors. The modulatory effects can activate a larger cortical area and thus allow cortical neurons to integrate a broader range of visual information to recognize complex cases. Additionally, to characterize spatial structures at various scales, a multiresolution technique is used to represent visual field information from fine to coarse. Different scale responses are combined to estimate the contour probability. Our method achieves state-of-the-art results among all biologically inspired contour detection models. This study provides a method for improving visual modeling of contour detection and inspires new ideas for integrating more brain cognitive mechanisms into deep neural networks.

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

A broad neuron-centric conception of contextual modulation is reviewed and re-assessed in the light of recent neurobiological studies of amplification, suppression, and synchronization. Behavioural and computational studies of perceptual and higher cognitive functions that depend on these processes are outlined, and evidence that those functions and their neuronal mechanisms are impaired in schizophrenia is summarized. Finally, we compare and assess the long-term biological functions of contextual modulation at the level of computational theory as formalized by the theories of coherent infomax and free energy reduction. We conclude that those theories, together with the many empirical findings reviewed, show how contextual modulation at the neuronal level enables the cortex to flexibly adapt the use of its knowledge to current circumstances by amplifying and grouping relevant activities and by suppressing irrelevant activities.

fMRI correlates of object-based attentional facilitation vs. suppression of irrelevant stimuli, dependent on global grouping and endogenous cueing

Theories of object-based attention often make two assumptions: that attentional resources are facilitatory, and that they spread automatically within grouped objects. Consistent with this, ignored visual stimuli can be easier to process, or more distracting, when perceptually grouped with an attended target stimulus. But in past studies, the ignored stimuli often shared potentially relevant features or locations with the target. In this fMRI study, we measured the effects of attention and grouping on Blood Oxygenation Level Dependent (BOLD) responses in the human brain to entirely task-irrelevant events. Two checkerboards were displayed each in opposite hemifields, while participants responded to check-size changes in one pre-cued hemifield, which varied between blocks. Grouping (or segmentation) between hemifields was manipulated between blocks, using common (vs. distinct) motion cues. Task-irrelevant transient events were introduced by randomly changing the color of either checkerboard, attended or ignored, at unpredictable intervals. The above assumptions predict heightened BOLD signals for irrelevant events in attended vs. ignored hemifields for ungrouped contexts, but less such attentional modulation under grouping, due to automatic spreading of facilitation across hemifields. We found the opposite pattern, in primary visual cortex. For ungrouped stimuli, BOLD signals associated with task-irrelevant changes were lower, not higher, in the attended vs. ignored hemifield; furthermore, attentional modulation was not reduced but actually inverted under grouping, with higher signals for events in the attended vs. ignored hemifield. These results challenge two popular assumptions underlying object-based attention. We consider a broader biased-competition framework: task-irrelevant stimuli are suppressed according to how strongly they compete with task-relevant stimuli, with intensified competition when the irrelevant features or locations comprise the same object.

The spatial range of contour integration deficits in schizophrenia

Contour integration (CI) refers to the process that represents spatially separated elements as a unified edge or closed shape. Schizophrenia is a psychiatric disorder characterized by symptoms such as hallucinations, delusions, disorganized thinking, inappropriate affect, and social withdrawal. Persons with schizophrenia are impaired at CI, but the specific mechanisms underlying the deficit are still not clear. Here, we explored the hypothesis that poor patient performance owes to reduced feedback or impaired longer-range lateral connectivity within early visual cortex--functionally similar to that found in 5- to 6-year old children. This hypothesis predicts that as target element spacing increases from .7 to 1.4° of visual angle, patient impairments will become more pronounced. As a test of the prediction, 25 healthy controls and 36 clinically stable, asymptomatic persons with schizophrenia completed a CI task that involved determining whether a subset of Gabor elements formed a leftward or rightward pointing shape. Adjacent shape elements were spaced at either .7 or 1.4° of visual angle. Difficulty in each spacing condition depended on the number of noise elements present. Patients performed worse than controls overall, both groups performed worse with the larger spacing, and the magnitude of the between-group difference was not amplified at the larger spacing. These results show that CI deficits in schizophrenia cannot be explained in terms of a reduced spatial range of integration, at least not when the shape elements are spaced within 1.5°. Later-developing, low-level integrative mechanisms of lateral connectivity and feedback appear not to be differentially impaired in the illness.

Visual cortex combines a stimulus and an error-like signal with a proportion that is dependent on time, space, and stimulus contrast

Even though the visual cortex is one of the most studied brain areas, the neuronal code in this area is still not fully understood. In the literature, two codes are commonly hypothesized, namely stimulus and predictive (error) codes. Here, we examined whether and how these two codes can coexist in a neuron. To this end, we assumed that neurons could predict a constant stimulus across time or space, since this is the most fundamental type of prediction. Prediction was examined in time using electrophysiology and voltage-sensitive dye imaging in the supragranular layers in area 18 of the anesthetized cat, and in space using a computer model. The distinction into stimulus and error code was made by means of the orientation tuning of the recorded unit. The stimulus was constructed as such that a maximum response to the non-preferred orientation indicated an error signal, and the maximum response to the preferred orientation indicated a stimulus signal. We demonstrate that a single neuron combines stimulus and error-like coding. In addition, we observed that the duration of the error coding varies as a function of stimulus contrast. For low contrast the error-like coding was prolonged by around 60-100%. Finally, the combination of stimulus and error leads to a suboptimal free energy in a recent predictive coding model. We therefore suggest a straightforward modification that can be applied to the free energy model and other predictive coding models. Combining stimulus and error might be advantageous because the stimulus code enables a direct stimulus recognition that is free of assumptions whereas the error code enables an experience dependent inference of ambiguous and non-salient stimuli.

Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function

A method is presented for learning the reciprocal feedforward and feedback connections required by the predictive coding model of cortical function. When this method is used, feedforward and feedback connections are learned simultaneously and independently in a biologically plausible manner. The performance of the proposed algorithm is evaluated by applying it to learning the elementary components of artificial and natural images. For artificial images, the bars problem is employed, and the proposed algorithm is shown to produce state-of-the-art performance on this task. For natural images, components resembling Gabor functions are learned in the first processing stage, and neurons responsive to corners are learned in the second processing stage. The properties of these learned representations are in good agreement with neurophysiological data from V1 and V2. The proposed algorithm demonstrates for the first time that a single computational theory can explain the formation of cortical RFs and also the response properties of cortical neurons once those RFs have been learned.

Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function

The combination of two or more population-coded signals in a neural model of predictive coding can give rise to multiplicative gain modulation in the response properties of individual neurons. Synaptic weights generating these multiplicative response properties can be learned using an unsupervised, Hebbian learning rule. The behavior of the model is compared to empirical data on gaze-dependent gain modulation of cortical cells and found to be in good agreement with a range of physiological observations. Furthermore, it is demonstrated that the model can learn to represent a set of basis functions. This letter thus connects an often-observed neurophysiological phenomenon and important neurocomputational principle (gain modulation) with an influential theory of brain operation (predictive coding).

Predictive coding as a model of response properties in cortical area V1

A simple model is shown to account for a large range of V1 classical, and nonclassical, receptive field properties including orientation tuning, spatial and temporal frequency tuning, cross-orientation suppression, surround suppression, and facilitation and inhibition by flankers and textured surrounds. The model is an implementation of the predictive coding theory of cortical function and thus provides a single computational explanation for a diverse range of neurophysiological findings. Furthermore, since predictive coding can be related to the biased competition theory and is a specific example of more general theories of hierarchical perceptual inference, the current results relate V1 response properties to a wider, more unified, framework for understanding cortical function.