Characterizing Receptive Field Selectivity In Area V2, 2015 New York University
Characterizing Receptive Field Selectivity In Area V2, Corey M. Ziemba, Robbe Lt Goris, J Anthony Movshon, Eero P. Simoncelli
The computations performed by neurons in area V1 are reasonably well understood, but computation in subsequent areas such as V2 have been more difficult to characterize. When stimulated with visual stimuli traditionally used to investigate V1, such as sinusoidal gratings, V2 neurons exhibit similar selectivity (but with larger receptive fields, and weaker responses) relative to V1 neurons. However, we find that V2 responses to synthetic stimuli designed to produce naturalistic patterns of joint activity in a model V1 population are more vigorous than responses to control stimuli that lacked this naturalistic structure (Freeman, et. al. 2013). Armed with this signature ...
‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, 2015 email@example.com
‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, Michael E. Rudd
In the ‘phantom’ illusion (Galmonte, Soranzo, Rudd, & Agostini, submitted), either an incremental or a decremental target, when surrounded by a luminance gradient, can to be made to appear as an increment or a decrement, depending on the gradient width. For wide gradients, incremental targets appear as increments and decremental targets appear as decrements. For narrow gradients, the reverse is true. Here, I model these phenomena with a two-stage neural lightness theory (Rudd, 2013, 2014) in which local steps in log luminance are first encoded by oriented spatial filters operating on a log-transformed version of the image; then the filter outputs are appropriately integrated along image paths directed towards the target. A contrast gain control mechanism adjusts each filter’s gain on the basis of the outputs of other nearby filters. The weighted contribution of each filter to the target lightness decays exponentially with distance, as do the strengths of the between-filter gain modulations. I simulate the lightnesses of incremental and decremental targets as a function of gradient width and show that the model reproduces the key properties of the phantom illusion, even when the gain applied to decremental luminance ...
Time-Lapse Statistics Of Cone Signals From Natural Scenes, 2015 University of Manchester
Time-Lapse Statistics Of Cone Signals From Natural Scenes, David H. Foster, Kinjiro Amano, Sérgio M C Nascimento
In the natural world, the spectrum and geometry of the illumination from the sun and sky vary over the day. These geometric changes make it especially difficult to extract from the reflected light invariant signals for surface color perception. The aim of this study was to test the utility of certain combinations of retinal cone excitations, in particular, spatial cone-excitation ratios, known to be approximately invariant under non-geometric changes in illumination. Hyperspectral radiance images were acquired at roughly hourly intervals in four outdoor scenes in the Minho region of Portugal. Spatial resolution of the camera was 1344×1024 pixels. Peak-transmission ...
The Role Of Gpr126 In Myelin Maintenance And Regeneration In The Peripheral Nervous System, 2015 Washington University in St. Louis
The Role Of Gpr126 In Myelin Maintenance And Regeneration In The Peripheral Nervous System, Jessica A. Joseph
Myelin is a multilayered insulating membrane that allows rapid propagation of electrical signals and provides neurotrophic and cellular support to axons. Specialized glial cells called oligodendrocytes synthesize myelin in the central nervous system (CNS), while Schwann cells myelinate axons in the peripheral nervous system (PNS). Irregular myelin formation or myelin degeneration leads to debilitating diseases like multiple sclerosis (MS) in the CNS and Charcot-Marie-Tooth Disease in the PNS. However, the molecular mechanisms behind myelin maintenance or regeneration after injury or disease are not well understood. Adhesion G Protein- Coupled Receptors (aGPCRs) are a unique class of GPCRs, in that they ...
The Bounded Log-Odds Model Of Frequency And Probability Distortion, 2015 Peking University
The Bounded Log-Odds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney
No abstract provided.
Putting Saliency In Its Place, 2015 York University
Putting Saliency In Its Place, John K. Tsotsos
The role of attention and the place within the visual processing stream where the concept of saliency has been situated is critically examined by considering the experimental evidence and performing tests that link experiment to computation.
Towards A Unified Computational Model Of Contextual Interactions Across Visual Modalities, 2015 Brown University
Towards A Unified Computational Model Of Contextual Interactions Across Visual Modalities, David A. Mély, Thomas Serre
The perception of a stimulus is largely determined by its surrounding. Examples abound from color (Land and McCann, 1971), disparity (Westheimer, 1986) and motion induction (Anstis and Casco, 2006) to orientation tilt effects (O’Toole and Wenderoth, 1976). Some of these phenomena have been studied individually using monkey neurophysiology techniques. In these experiments, a center stimulus is typically used to probe a cell’s classical “center” receptive field (cRF), whose activity is then modulated by an annular “surround” (extra-cRF) stimulus. While this center-surround integration (CSI) has been well characterized, a theoretical framework which unifies these different phenomena across visual modalities ...
A Conceptual Framework Of Computations In Mid-Level Vision, 2015 University of Leuven
A Conceptual Framework Of Computations In Mid-Level Vision, Jonas Kubilius, Johan Wagemans, Hans P. Op De Beeck
The goal of visual processing is to extract information necessary for a variety of tasks, such as grasping objects, navigating in scenes, and recognizing them. While ultimately these tasks might be carried out by separate processing pathways, they nonetheless share a common root in the early and intermediate visual areas. What representations should these areas develop in order to facilitate all of these higher-level tasks? Several distinct ideas have received empirical support in the literature so far: (i) boundary feature detection, such as edge, corner, and curved segment extraction; (ii) second-order feature detection, such as the difference in orientation or ...
Binocular 3d Motion Perception As Bayesian Inference, 2015 University of Glasgow
Binocular 3d Motion Perception As Bayesian Inference, Martin Lages, Suzanne Heron
The human visual system encodes monocular motion and binocular disparity input before it is integrated into a single 3D percept. Here we propose a geometric-statistical model of human 3D motion perception that solves the aperture problem in 3D by assuming that (i) velocity constraints arise from inverse projection of local 2D velocity constraints in a binocular viewing geometry, (ii) noise from monocular motion and binocular disparity processing is independent, and (iii) slower motions are more likely to occur than faster ones. In two experiments we found that instantiation of this Bayesian model can explain perceived 3D line motion direction under ...
A Binocular Model For Motion Integration In Mt Neurons, 2015 University of Washington - Seattle Campus
A Binocular Model For Motion Integration In Mt Neurons, Pamela M. Baker, Wyeth Bair
Processing of visual motion by neurons in MT has long been an active area of study, however circuit models detailing the computations underlying binocular integration of motion signals remains elusive. Such models are important for studying the visual perception of motion in depth (MID), which involves both frontoparallel (FP) visual motion and binocular signal integration. Recent studies (Czuba et al. 2014, Sanada and DeAngelis 2014) have shown that many MT neurons are MID sensitive, contrary to the prevailing view (Maunsell and van Essen, 1983). These novel data are ideal for constraining models of binocular motion integration in MT. We have ...
A Critical Evaluation Of Computational Mechanisms Of Binocular Disparity Processing, 2015 Brown University
A Critical Evaluation Of Computational Mechanisms Of Binocular Disparity Processing, Junkyung Kim, David A. Mély, Thomas Serre
The past decades of research in visual neuroscience have generated a large and disparate body of literature on the computation of binocular disparity in the primary visual cortex. Models have been proposed to describe specific phenomena, yet we lack a theoretical framework which is grounded in neurophysiology and also explains the effectiveness of disparity computation. Here, we examine neural circuits that are thought to play an important role in the computation of binocular disparity. Starting with the binocular energy model (Ohzawa et al. 1990), we consider plausible extensions which include suppressive mechanisms from units tuned to different phase disparities (Tanabe ...
Modeling Shape Representation In Area V4, 2015 University of Washington
Modeling Shape Representation In Area V4, Wyeth Bair, Dina Popovkina, Abhishek De, Anitha Pasupathy
Our model builds on a convolutional-style neural network with hierarchical stages representing processing steps in the ventral visual pathway. It was designed to capture the translation-invariance and shape-selectivity of neurons in area V4. The model uses biologically plausible linear filters at the front end, normalization and sigmoidal nonlinear activation functions. The max() function is used to generate translation invariance.
A Recurrent Multilayer Model With Hebbian Learning And Intrinsic Plasticity Leads To Invariant Object Recognition And Biologically Plausible Receptive Fields, 2015 Chemnitz University of Technology
A Recurrent Multilayer Model With Hebbian Learning And Intrinsic Plasticity Leads To Invariant Object Recognition And Biologically Plausible Receptive Fields, Michael Teichmann, Fred H. Hamker
Much effort has been spent to develop biologically plausible models for different aspects of the processing in the visual cortex. However, most of these models are not investigated with respect to the functionality of the neural code for the purpose of object recognition comparable to the framework of deep learning in the machine learning community.
We developed a model of V1 and V2 based on anatomical evidence of the layered architecture, using excitatory and inhibitory neurons where the connectivity to each neuron is learned in parallel. We address learning by three different mechanisms of plasticity: intrinsic plasticity, Hebbian learning with ...
A Computational Model To Account For Dynamics Of Spatial Updating Of Remembered Visual Targets Across Slow And Rapid Eye Movements, 2015 Centre for Vision Research, York University
A Computational Model To Account For Dynamics Of Spatial Updating Of Remembered Visual Targets Across Slow And Rapid Eye Movements, Yalda Mohsenzadeh, J. Douglas Crawford
Despite the ever-changing visual scene on the retina between eye movements, our perception of the visual world is constant and unified. It is generally believed that this space constancy is due to the brain’s ability of spatial updating. Although many efforts have been made to discover the mechanism underlying spatial updating across eye movements, still there are many unanswered questions about the neuronal mechanism of this phenomenon.
We developed a state space model for updating gaze-centered spatial information. To explore spatial updating, we considered two kinds of eye movements, saccade and smooth pursuit. The inputs to our proposed model ...
A Model Of Repetitive Microsaccades, Coupled With Pre-Microsaccadic Changes In Vision, Is Sufficient To Account For Both Attentional Capture And Inhibition Of Return In Posner Cueing, 2015 Werner Reichardt Centre for Integrative Neuroscience, Tuebingen University
A Model Of Repetitive Microsaccades, Coupled With Pre-Microsaccadic Changes In Vision, Is Sufficient To Account For Both Attentional Capture And Inhibition Of Return In Posner Cueing, Ziad M. Hafed, Xiaoguang Tian
When a cue is presented at a location, orienting efficacy towards that location is improved relative to other locations (“attentional capture”), but only briefly; a mere few hundred milliseconds later, orienting incurs large costs. These costs have been classically termed “inhibition of return” (IOR), alluding to voluntary, cognitive strategies avoiding perseverance at one location. However, despite this popular hypothesis, the origins of both attentional capture and IOR remain elusive. Here we show that both of these phenomena can be accounted for by a single concept of oculomotor rhythmicity, and one that involves the entire gamut of saccadic activity including microsaccades ...
Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, 2015 Chemnitz University of Technology
Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker
Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to real-world tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.
To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by object-view specific neurons, learned via a trace learning approach (Antonelli et al., 2014, IEEE TAMD). A visual cortex model combines the microcircuit with neuroanatomical properties like top-down attentional processing, hierarchical-increasing receptive field sizes, and synaptic transmission delays. The visual cortex model is complemented by a model of the frontal eye field (Zirnsak et al., 2011, Eur J Neurosci).
We evaluated the model on a realistic object recognition task in which a given target has to be localized in a scene (guided visual search task), using 100 different target objects, 1000 scenes, and two backgrounds. The model achieves an accuracy of 92% at black, and of 71% at white-noise backgrounds. We found that two of the underlying, neuronal attention mechanisms are prominently relevant for guided visual search: amplification of neurons preferring the target; and suppression of neurons encoding distractors or background noise.
Modeling Visual Features To Recognize Biological Motion: A Developmental Approach, 2015 Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Italy
Modeling Visual Features To Recognize Biological Motion: A Developmental Approach, Giulio Sandini, Nicoletta Noceti, Alessandra Sciutti, Francesco Rea, Alessandro Verri, Francesca Odone
In this work we deal with the problem of designing and developing computational vision models – comparable to the early stages of the human development – using coarse low-level information.
More specifically, we consider a binary classification setting to characterize biological movements with respect to non-biological dynamic events. To this purpose, our model builds on top of the optical flow estimation, and abstract the representation to simulate the limited amount of visual information available at birth. We take inspiration from known biological motion regularities explained by the Two-Thirds Power Law, and design a motion representation that includes different low-level features, which can ...
A Novel Gabaergic Projection Links Central Amygdala To Frontal Cortex And Facilitates Reward-Like Behavior, 2015 Washington University in St Louis
A Novel Gabaergic Projection Links Central Amygdala To Frontal Cortex And Facilitates Reward-Like Behavior, Samuel C. Funderburk
Undergraduate Research Symposium Posters & Abstracts
The neural circuitry underlying mammalian reward behaviors comprises several distinct nuclei throughout the brain. Previous research has indicated that inhibiting the infra-limbic area of the frontal cortex is rewarding to the animal, while activating the central amygdala during reward presentation increases future preference for that reward. Using viral vector-mediated, cell-type specific viral fluorescence tracing in transgenic mice, we identified a GABAergic projection originating in the central amygdala (CeA) that terminates in the infra-limbic area of the frontal cortex. Follow-up experiments to optogenetically activate this circuit in awake, behaving animals produced reward-like behaviors. We hypothesize that activity in these cells inhibits ...
Complementary Effect Of Electrical And Inhibitory Coupling In Bursting Synchronization, 2015 Georgia State University
Complementary Effect Of Electrical And Inhibitory Coupling In Bursting Synchronization, Kevin Daley
Georgia State Undergraduate Research Conference
Effects Of Meaningfulness In Left Angular Gyrus And Right Insula, 2015 Georgia State University
Effects Of Meaningfulness In Left Angular Gyrus And Right Insula, Anna Nowaczyk
Georgia State Undergraduate Research Conference
No abstract provided.