A theory of a saliency map in primary visual cortex (V1) tested by psychophysics of color-orientation interference in texture segmentation

It has been proposed that V1 creates a bottom-up saliency map, where saliency of any location increases with the firing rate of the most active V1 output cell responding to it, regardless the feature selectivity of the cell. Thus, a red vertical bar may have its saliency signalled by a cell tuned to red colour, or one tuned to vertical orientation, whichever cell is the most active. This theory predicts interference between colour and orientation features in texture segmentation tasks where bottom-up processes are significant. The theory not only explains existing data, but also provides a prediction. A subsequent psychophysical test confirmed the prediction by showing that segmentation of textures of oriented bars became more difficult as the colours of the bars were randomly drawn from more colour categories

Mathematical analysis and simulations of the neural circuit for locomotion in lamprey

We analyze the dynamics of the neural circuit of the lamprey central pattern generator. This analysis provides insight into how neural interactions form oscillators and enable spontaneous oscillations in a network of damped oscillators, which were not apparent in previous simulations or abstract phase oscillator models. We also show how the different behavior regimes (characterized by phase and amplitude relationships between oscillators) of forward or backward swimming, and turning, can be controlled using the neural connection strengths and external inputs

Contrast-reversed binocular dot-pairs in random-dot stereograms for depth perception in central visual field: Probing the dynamics of feedforward-feedback processes in visual inference

In a random-dot stereogram (RDS), the spatial disparities between the interocularly corresponding black and white random dots determine the depths of object surfaces. If a black dot in one monocular image corresponds to a white dot in the other, disparity-tuned neurons in primary visual cortex (V1) respond as if their preferred disparities become non-preferred and vice versa, reversing the disparity sign reported to higher visual areas. Reversed depth is perceptible in the peripheral but not the central visual field. This study demonstrates that, in central vision, adding contrast-reversed dots to a noisy RDS (containing the normal contrast-matched dots) can augment or degrade depth perception. Augmentation occurs when the reversed depth signals are congruent with the normal depth signals to report the same disparity sign, and occurs regardless of the viewing duration. Degradation occurs when the reversed and normal depth signals are incongruent with each other and when the RDS is viewed briefly. These phenomena reflect the Feedforward-Feedback-Verify-and-reWeight (FFVW) process for visual inference in central vision, and are consistent with the central-peripheral dichotomy that central vision has a stronger top-down feedback from higher to lower brain areas to disambiguate noisy and ambiguous inputs from V1. When a RDS is viewed too briefly for feedback, augmentation and degradation work by adding the reversed depth signals from contrast-reversed dots to the feedforward, normal, depth signals. With a sufficiently long viewing duration, the feedback vetoes incongruent reversed depth signals and amends or completes the imperfect, but congruent, reversed depth signals by analysis-by-synthesis computation

From the optic tectum to the primary visual cortex: migration through evolution of the saliency map for exogenous attentional guidance

Recent data have supported the hypothesis that, in primates, the primary visual cortex (V1) creates a saliency map from visual input. The exogenous guidance of attention is then realized by means of monosynaptic projections to the superior colliculus, which can select the most salient location as the target of a gaze shift. V1 is less prominent, or is even absent in lower vertebrates such as fish; whereas the superior colliculus, called optic tectum in lower vertebrates, also receives retinal input. I review the literature and propose that the saliency map has migrated from the tectum to V1 over evolution. In addition, attentional benefits manifested as cueing effects in humans should also be present in lower vertebrates

A V1 model of pop out and asymmetry in visual search

Visual search is the task of finding a target in an image against a background of distractors. Unique features of targets enable them to pop out against the background, while targets defined by lacks of features or conjunctions of features are more difficult to spot. It is known that the ease of target detection can change when the roles of figure and ground are switched. The mechanisms underlying the ease of pop out and asymmetry in visual search have been elusive. This paper shows that a model of segmentation in V1 based on intracortical interactions can explain many of the qualitative aspects of visual search

Feedback from higher to lower visual areas for visual recognition may be weaker in the periphery: Glimpses from the perception of brief dichoptic stimuli

Eye movements bring attended visual inputs to the center of vision for further processing. Thus, central and peripheral vision should have different functional roles. Here, we use observations of visual perception under dichoptic stimuli to infer that there is a difference in the top-down feedback from higher brain centers to primary visual cortex. Visual stimuli to the two eyes were designed such that the sum and difference of the binocular input from the two eyes have the form of two different gratings. These gratings differed in their motion direction, tilt direction, or color, and duly evoked ambiguous percepts for the corresponding feature. Observers were more likely to perceive the feature in the binocular summation rather than the difference channel. However, this perceptual bias towards the binocular summation signal was weaker or absent in peripheral vision, even when central and peripheral vision showed no difference in contrast sensitivity to the binocular summation signal relative to that to the binocular difference signal. We propose that this bias can arise from top-down feedback as part of an analysis-by-synthesis computation. The feedback is of the input predicted using prior information by the upper level perceptual hypothesis about the visual scene; the hypothesis is verified by comparing the feedback with the actual visual input. We illustrate this process using a conceptual circuit model. In this framework, a bias towards binocular summation can arise from the prior knowledge that inputs are usually correlated between the two eyes. Accordingly, a weaker bias in the periphery implies that the top-down feedback is weaker there. Testable experimental predictions are presented and discussed

Understanding Auditory Spectro-Temporal Receptive Fields and Their Changes with Input Statistics by Efficient Coding Principles

Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations to the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on statistical attributes of the stimuli, such as sound intensity, is usually explained by nonlinear mechanisms and models. Here, we apply an efficient coding principle which has been successfully used to understand receptive fields in early stages of visual processing, in order to provide a computational understanding of the STRFs. According to this principle, STRFs result from an optimal tradeoff between maximizing the sensory information the brain receives, and minimizing the cost of the neural activities required to represent and transmit this information. Both terms depend on the statistical properties of the sensory inputs and the noise that corrupts them. The STRFs should therefore depend on the input power spectrum and the signal-to-noise ratio, which is assumed to increase with input intensity. We analytically derive the optimal STRFs when signal and noise are approximated as Gaussians. Under the constraint that they should be spectro-temporally local, the STRFs are predicted to adapt from being band-pass to low-pass filters as the input intensity reduces, or the input correlation becomes longer range in sound frequency or time. These predictions qualitatively match physiological observations. Our prediction as to how the STRFs should be determined by the input power spectrum could readily be tested, since this spectrum depends on the stimulus ensemble. The potentials and limitations of the efficient coding principle are discussed

Properties of V1 Neurons Tuned to Conjunctions of Visual Features: Application of the V1 Saliency Hypothesis to Visual Search behavior

From a computational theory of V1, we formulate an optimization problem to investigate neural properties in the primary visual cortex (V1) from human reaction times (RTs) in visual search. The theory is the V1 saliency hypothesis that the bottom-up saliency of any visual location is represented by the highest V1 response to it relative to the background responses. The neural properties probed are those associated with the less known V1 neurons tuned simultaneously or conjunctively in two feature dimensions. The visual search is to find a target bar unique in color (C), orientation (O), motion direction (M), or redundantly in combinations of these features (e.g., CO, MO, or CM) among uniform background bars. A feature singleton target is salient because its evoked V1 response largely escapes the iso-feature suppression on responses to the background bars. The responses of the conjunctively tuned cells are manifested in the shortening of the RT for a redundant feature target (e.g., a CO target) from that predicted by a race between the RTs for the two corresponding single feature targets (e.g., C and O targets). Our investigation enables the following testable predictions. Contextual suppression on the response of a CO-tuned or MO-tuned conjunctive cell is weaker when the contextual inputs differ from the direct inputs in both feature dimensions, rather than just one. Additionally, CO-tuned cells and MO-tuned cells are often more active than the single feature tuned cells in response to the redundant feature targets, and this occurs more frequently for the MO-tuned cells such that the MO-tuned cells are no less likely than either the M-tuned or O-tuned neurons to be the most responsive neuron to dictate saliency for an MO target