Visual field

The mapping from the visual field to V1 can be approximated by a log-polar transform. In this domain, scale is a left-right shift, and rotation is an up-down shift. When fed into a standard shift-invariant convolutional network (CNN), this provides scale and rotation invariance. Remarkably, despite the property of rotation invariance, this model provides a novel explanation for the inverted face effect. The effect comes about because, while the scale shift maps directly onto the flat cortex, rotation, as a circle group, does not. When the face is inverted, the features simply shift up and dow

…For instance, the visual system enacts compensatory eye movements based on a global motion representation, generated by combining local motion signals from the retina. However, due to the aperture problem, local motion signals are inherently ambiguous, and how the visual system solves the aperture problem is unclear. We studied how the mouse Nucleus of the Optic Tract (NOT) integrates retinal visual information and how this supports behavioral responses. NOT mediates the horizontal optokinetic reflex, the com

The mapping from the visual field to V1 can be approximated by a log-polar transform. In this domain, scale is a left-right shift, and rotation is an up-down shift. When fed into a standard shift-invariant convolutional network, this provides scale and rotation invariance. However, translation invariance is lost. In our model, this is compensated for by multiple fixations on an object. Due to the high concentration of cones in the fovea with the dropoff of resolution in the periphery, fully 10 degrees of visual angle take up about half of V1, with the remaining 170 degrees (or so) taking up t

Recurrent network models of excitatory (E) and inhibitory (I) neurons with supralinear activation functions have successfully explained several cortical computations, including response normalization and surround suppression. Unlike more abstract approaches, such networks allow direct comparisons with experimentally measured neural activities and synaptic strengths. However, the scope of these networks remained limited as their connectivity needed to be designed by hand or fitted by complex machine learning algorithms to yield stable activity and computations. Here we present a method to effic

How does the visual system of insects function in vastly different light intensities, process separate parts of the visual field in parallel, and cope with eye sizes that differ between individuals? This talk will give you the answers we receive from our unique(ly adorable) model system: hawkmoths.

…and architecture -- from receptive field (RF) shapes to RF mosaic alignment -- are optimized for efficient sensory coding. These adaptations have been primarily studied in small regions of the retina, however stimulus statistics vary systematically across the entire visual field (VF). It therefore remains unknown whether the collective structure of large neural p

The talk will give a tour of Guided Search 6.0 (GS6), the latest evolution of Guided Search. Part 1 describes The Mechanics of Search. Because we cannot recognize more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more frequently than that, it follo