Neuroscience and Neurobiology Commons^™

Central And Peripheral Difference In Perceptual Bias In Ambiguous Perception Using Dichoptic Stimuli --- Implications For The Analysis-By-Synthesis Process In Visual Recognition, Li Zhaoping Prof

MODVIS Workshop

No abstract provided.

Predicting Fixations From Deep And Low-Level Features, Matthias Kümmerer, Thomas S.A. Wallis, Leon A. Gatys, Matthias Bethge

MODVIS Workshop

Learning what properties of an image are associated with human gaze placement is important both for understanding how biological systems explore the environment and for computer vision applications. Recent advances in deep learning for the first time enable us to explain a significant portion of the information expressed in the spatial fixation structure. Our saliency model DeepGaze II uses the VGG network (trained on object recognition in the ImageNet challenge) to convert an image into a high-dimensional feature space which is then readout by a second very simple network to yield a density prediction. DeepGaze II is right now the …

Mapping The Spatio-Temporal Dynamics Of Vision In The Human Brain, Aude Oliva

MODVIS Workshop

Recognition of objects and scenes is a fundamental function of the human brain, necessitating a complex neural machinery that transforms low level visual information into semantic content. Despite significant advances in characterizing the locus and function of key visual areas, integrating the temporal and spatial dynamics of this processing stream has posed a decades-long challenge to human neuroscience. In this talk I will describe a brain mapping approach to combine magnetoencephalography (MEG), functional MRI (fMRI) measurements, and convolutional neural networks (CNN) by representational similarity analysis to yield a spatially and temporally integrated characterization of neuronal representations when observers perceive visual …

Neural Computation Of Statistical Image Properties In Peripheral Vision, Christoph Zetzsche, Ruth Rosenholtz, Noshaba Cheema, Konrad Gadzicki, Lex Fridman

MODVIS Workshop

No abstract provided.

Similarity-Based Fusion Of Meg And Fmri Discerns Early Feedforward And Feedback Processing In The Ventral Stream, Yalda Mohsenzadeh Dr., Radoslaw Martin Cichy Dr., Aude Oliva Dr., Dimitrios Pantazis Dr.

MODVIS Workshop

Successful models of vision, such as DNNs and HMAX, are inspired by the human visual system, relying on a hierarchical cascade of feedforward transformations akin to the ventral stream. Despite these advances, the human visual cortex remains unique in complexity, with feedforward and feedback pathways characterized by rapid spatiotemporal dynamics as visual information is transformed into semantic content. Thus, a systematic characterization of the spatiotemporal and representational space of the ventral visual pathway can offer novel insights in the duration and sequencing of cognitive processes, suggesting computational constraints and new architectures for computer vision models.

To discern the feedforward and …

Using Classification Images To Understand Models Of Lightness Perception, Minjung Kim, Jason M. Gold, Richard F. Murray

MODVIS Workshop

No abstract provided.

Edge Integration And Image Segmentation In Lightness And Color: Computational And Neural Theory, Michael E. Rudd

MODVIS Workshop

No abstract provided.

Modeling Accommodation Control Of The Human Eye: Chromatic Aberration And Color Opponency, Agostino Gibaldi, Steven A. Cholewiak, Marty S. Banks

MODVIS Workshop

Accommodation is the process by which the eye lens changes optical power to maintain a clear retinal image as the distance to the fixated object varies. Although luminance blur has long been considered the driving feature for accommodation, it is by definition unsigned (i.e., there is no difference between the defocus of an object closer or farther than the focus distance). Nonetheless, the visual system initially accommodates in the correct direction, implying that it exploits a cue with sign information. Here, we present a model of accommodation control based on such a cue: Longitudinal Chromatic Aberration (LCA). The model relies …

Can Cone Signals In The Wild Be Predicted From The Past?, David H. Foster, Iván Marín-Franch

MODVIS Workshop

In the natural world, the past is usually a good guide to the future. If light from the sun and sky is blue earlier in the day and yellow now, then it is likely to be more yellow later, as the sun's elevation decreases. But is the light reflected from a scene into the eye as predictable as the light incident upon the scene, especially when lighting changes are not just spectral but include changes in local shadows and mutual reflections? The aim of this work was to test the predictability of cone photoreceptor signals in the wild over the …

Role Of The Cost Of Plasticity In Determining The Features Of Fast Vision In Humans., Maria M. Del Viva Phd, Renato Budinich M. Sc, Laura Palmieri M. Sc, Vladimir S Georgiev Phd, Giovanni Punzi Phd

MODVIS Workshop

No abstract provided.

Modeling The Mechanisms Of Reward Learning That Bias Visual Attention, Jason Hays, Fabian Soto Phd

MODVIS Workshop

No abstract provided.

Heuristics From Statistics—Modeling The Behavior And Perception Of Non-Rigid Materials, Vivian C. Paulun, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Modelling Grip Point Selection In Human Precision Grip, Guido Maiello, Lina Klein, Vivian C. Paulun, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Comparing Diverse V1 Models On The Same Platform: Virtual V1sion, Cheryl Olman

MODVIS Workshop

No abstract provided.

Unifying Binocular, Spatial, And Spatio-Temporal Frequency Integration In Models Of Mt Neurons, Pamela M. Baker, Wyeth Bair

MODVIS Workshop

No abstract provided.

Evaluating And Interpreting A Convolutional Neural Net As A Model Of V4, Dean A. Pospisil, Anitha Pasupathy, Wyeth Bair

MODVIS Workshop

Convolutional neural nets (CNNs) are currently the highest performing image recognition computer algorithms. Of interest is whether these CNNs, following extensive supervised training, perform computations similar to those in the ventral visual stream. We investigated whether CNN units’ tuning for shape boundaries was similar to V4’s as described in the angular position and curvature (APC) model of Pasupathy and Connor 2001. From units in all layers of AlexNet (see Figure A), an object recognition CNN, we recorded responses to the original study’s set of shape stimuli (51 simple closed shapes at up to 8 rotations) presented at 51 spatial translations …

Gabor Limits And Hyper-Selectivity In The Tuning Of V1 Neurons, David J. Field, Kedarnath P. Vilankar

MODVIS Workshop

No abstract provided.