Visual attention: The past 25 years

Mathematica--A System for Doing Mathematics by Computer.

The small visual area known as MT or V5 has played a major role in our understanding of the primate cerebral cortex. This area has been historically important in the concept of cortical processing streams and the idea that different visual areas constitute highly specialized representations of visual information. MT has also proven to be a fertile culture dish--full of direction- and disparity-selective neurons--exploited by many labs to study the neural circuits underlying computations of motion and depth and to examine the relationship between neural activity and perception. Here we attempt a synthetic overview of the rich literature on MT with the goal of answering the question, What does MT do?

/pdf/structure-and-function-of-visual-area-mt-5cgljbd95o.pdf

Structure and Function of Visual Area MT

Perceiving other people's behaviors activates imitative motor plans in the perceiver, but there is disagreement as to the function of this activation. In contrast to other recent proposals (e.g., that it subserves overt imitation, identification and understanding of actions, or working memory), here it is argued that imitative motor activation feeds back into the perceptual processing of conspecifics' behaviors, generating top-down expectations and predictions of the unfolding action. Furthermore, this account incorporates recent ideas about emulators in the brain-mental simulations that run in parallel to the external events they simulate-to provide a mechanism by which motoric involvement could contribute to perception. Evidence from a variety of literatures is brought to bear to support this account of perceiving human body movement.

The case for motor involvement in perceiving conspecifics.

Top-down modulation of sensory processing allows the animal to select inputs most relevant to current tasks. We found that the cingulate (Cg) region of the mouse frontal cortex powerfully influences sensory processing in the primary visual cortex (V1) through long-range projections that activate local γ-aminobutyric acid-ergic (GABAergic) circuits. Optogenetic activation of Cg neurons enhanced V1 neuron responses and improved visual discrimination. Focal activation of Cg axons in V1 caused a response increase at the activation site but a decrease at nearby locations (center-surround modulation). Whereas somatostatin-positive GABAergic interneurons contributed preferentially to surround suppression, vasoactive intestinal peptide-positive interneurons were crucial for center facilitation. Long-range corticocortical projections thus act through local microcircuits to exert spatially specific top-down modulation of sensory processing.

/pdf/long-range-and-local-circuits-for-top-down-modulation-of-58a9w3rj67.pdf

Long-range and local circuits for top-down modulation of visual cortex processing

In the middle temporal (MT) area of primates, many motion-sensitive neurons with a wide range of preferred directions respond to a stimulus moving in a single direction. These neurons are involved in direction perception, but it is not clear how perceptual decisions are related to the population response. We recorded the activities of MT neurons in rhesus monkeys while they discriminated closely related directions, and examined the relationship between the activities of neurons tuned to different directions and the monkeys' choices. Perceptual decisions were significantly correlated with the activities of the highest-precision neurons but not with those of the lowest-precision neurons. The combined performance of the high-precision neurons matched the monkeys' behavior, whereas the ability to predict behavior based on the entire active population was poor. These results suggest that fine discrimination decisions are crucially dependent on the activities of the most informative neurons.

Neural population code for fine perceptual decisions in area MT

The time it takes to transmit information along the human visual pathways introduces a substantial delay in the processing of images that fall on the retina. This visual latency might be expected to cause a moving object to be perceived at a position behind its actual one, disrupting the accuracy of visually guided motor actions such as catching or hitting, but this does not happen. It has been proposed that the perceived position of a moving object is extrapolated forwards in time to compensate for the delay in visual processing1,2,3.

Moving ahead through differential visual latency

The primary visual cortex (V1) receives its driving input from the eyes via the lateral geniculate nucleus (LGN) of the thalamus. The lateral pulvinar nucleus of the thalamus also projects to V1, but this input is not well understood. We manipulated lateral pulvinar neural activity in prosimian primates and assessed the effect on supra-granular layers of V1 that project to higher visual cortex. Reversibly inactivating lateral pulvinar prevented supra-granular V1 neurons from responding to visual stimulation. Reversible, focal excitation of lateral pulvinar receptive fields increased the visual responses in coincident V1 receptive fields fourfold and shifted partially overlapping V1 receptive fields toward the center of excitation. V1 responses to regions surrounding the excited lateral pulvinar receptive fields were suppressed. LGN responses were unaffected by these lateral pulvinar manipulations. Excitation of lateral pulvinar after LGN lesion activated supra-granular layer V1 neurons. Thus, lateral pulvinar is able to powerfully control and gate information outflow from V1.

/pdf/gating-and-control-of-primary-visual-cortex-by-pulvinar-58k37nogor.pdf

Gating and control of primary visual cortex by pulvinar

This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks (FFNNs) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNNs can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNNs are capable of recognizing structures in data, a property that conventional FFNNs with sigmoidal hidden units lack.

Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

Backward-masking functions have been hitherto categorized into two types, commonly named Type A and Type B. The analysis of a model of Retino-Cortical Dynamics produces the prediction that spatially localized stimuli should reveal an oscillatory metacontrast function. The predicted new type of metacontrast masking function was investigated in a psychophysical experiment. The results show oscillatory metacontrast functions with significant power in the gamma range (30-70 Hz). A marked decrease in the oscillations is observed when the spatial extent of the stimuli is increased. The theoretical basis of the study relates the oscillations found in the metacontrast function to gamma-range oscillations observed in scalp and intracerebral recordings. The qualitative agreement between the model and data provides support for this putative relationship.

Gopathy Purushothaman

Papers

Neural population code for fine perceptual decisions in area MT

Moving ahead through differential visual latency

Gating and control of primary visual cortex by pulvinar

Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

Gamma-range oscillations in backward-masking functions and their putative neural correlates