Biology subjects

Iyer, A. V.

Publications and source records attributed to Iyer, A. V..

2 recordsLinked to original sources

bioRxiv2018

Model neuron response statistics to natural images

To model the responses of neurons in the early visual system, at least three basic components are required: a receptive field, a normalization term, and a specification of encoding noise. Here, we examine how the receptive field, the normalization factor, and the encoding noise impact the model neuron responses to natural images and the signal-to-noise ratio for natural image discrimination. We show that when these components are modeled appropriately, the model neuron responses to natural stimuli are Gaussian distributed, scale-invariant, and very nearly maximize the signal-to-noise ratio for stimulus discrimination. We discuss the statistical models of natural stimuli that can account for these response statistics, and we show how some commonly used modeling practices may distort these results. Finally, we show that normalization can equalize important properties of neural response across different stimulus types. Specifically, narrowband (stimulus- and feature-specific) normalization causes model neurons to yield Gaussian-distributed responses to natural stimuli, 1/f noise stimuli, and white noise stimuli. The current work makes recommendations for best practices and it lays a foundation, grounded in the response statistics to natural stimuli, upon which principled models of more complex visual tasks can be built.

neuroscience↗

bioRxiv2017

The effect of depth variation on disparity tasks in natural scenes

Local depth variation is a distinctive property of natural scenes and its effects on perception have only recently begun to be investigated. Here, we demonstrate how natural depth variation impacts performance in two fundamental tasks related to stereopsis: half-occlusion detection and disparity detection. We report the results of a computational study that uses a large database of calibrated natural stereo-images with precisely co-registered laser-based distance measurements. First, we develop a procedure for precisely sampling stereo-image patches from the stereo-images, based on the distance measurements. The local depth variation in each stereo-image patch is quantified by disparity contrast. Next, we show that increased disparity contrast degrades performance in half-occlusion detection and disparity detection tasks, and changes the size and shape of the optimal spatial integration areas (\"receptive fields\") for computing the task-relevant decision variables. Then, we show that a simple binocular image statistic predicts disparity contrast in natural scenes. Finally, we report results on the most likely patterns of disparity variation in natural scenes. Our findings motivate computational and psychophysical investigations of the mechanisms that underlie disparity estimation in local regions of natural scenes.

neuroscience↗