Neuroscience and Neurobiology Commons^™

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.

Ros Regulation Of Axonal Mitochondrial Transport, Pin-Chao Liao

Open Access Dissertations

Mitochondria perform critical functions including aerobic ATP production and calcium (Ca2+) homeostasis, but are also a major source of reactive oxygen species (ROS) production. To maintain cellular function and survival in neurons, mitochondria are transported along axons, and accumulate in regions with high demand for their functions. Oxidative stress and abnormal mitochondrial axonal transport are associated with neurodegenerative disorders. However, we know little about the connection between these two. Using primaryDrosophila neuronal cell culture and the third instar larval nervous system as in vitro and in vivo models, respectively, we studied mitochondrial transport under oxidative stress conditions. In vitro …

Dopamine D1 Receptor Activity In The Basolateral Amygdala Is Important For Mediating Fear, Reward And Safety Discrimination Learning, Ka Ho Ng

Open Access Theses

Post traumatic stress disorder (PTSD) patients frequently show impairment in safety learning (Jovanovic, Kazama, Bachevalier, & Davis, 2012). Since the amygdala is known to be critical for emotional processing(Wassum & Izquierdo, 2015) and dopamine signaling in the amygdala is important for mediating both fear and reward learning, current experiments examined the role of dopamine signaling in the BLA in mediating both safety learning and reward seeking. We manipulated dopamine D1 receptor activity with a D1 receptor agonist (SKF 38393) or D1 receptor antagonist (SCH23390) either systemically or infused directly into the BLA 20 minutes prior to training rats in a …

Characterizing The Effects Of Repetitive Head Trauma In Female Soccer Athletes For Prevention Of Mild Traumatic Brain Injury, Diana Otero Svaldi

Open Access Dissertations

As participation in women’s soccer continues to grow and the longevity of female athletes’ careers continues to increase, prevention of mTBI in women’s soccer has become a major concern for female athletes as the long-term risks associated with a history of mTBI are well documented. Among women’s sports, soccer exhibits the highest concussion rates, on par with those of men’s football at the collegiate level. Head impact monitoring technology has revealed that “concussive hits” occurring directly before symptomatic injury are not predictive of mTBI, suggesting that the cumulative effect of repetitive head impacts experienced by collision sport athletes should be …

Using Pupillometry To Characterize Visual Perception In Autistic Mouse Models, Chirag B. Patel, Samuel T. Kissinger, Alexandr Pak, Nicholas Dicola, Alexander A. Chubkyin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Fragile X syndrome (FXS) is the leading genetic cause of autism. Individuals with Fragile X Syndrome (FXS) commonly display social, behavioral, and intellectual disabilities. Perceptual deficits and their underlying neural activity remain poorly characterized in FXS and other autism spectrum disorders (ASD’s). To explore visual perception in autism, we developed camera based pupil tracking software using OpenCV (an open-source computer vision library) capable of measuring visually evoked changes in pupil area and position in the FXS mouse model (Fmr1 KO). Changes in pupil area and position are believed to correlate with changes in arousal or visual processing and may …

Mechanical Reliability Of Implantable Polyimide-Based Magnetic Microactuators For Biofouling Removal, Christian G. Figueroa-Espada, Qi Yang, Hyowon Lee

The Summer Undergraduate Research Fellowship (SURF) Symposium

Hydrocephalus is a neurological disorder that typically requires a long-term implantation of a shunt system to manage its symptoms. These shunt systems are notorious for their extremely high failure rate. More than 40% of all implanted shunt systems fail within the first year of implantation. On average, 85% of all hydrocephalus patients with shunt systems undergo at least two shunt-revision surgeries within 10 years of implantation. A large portion of this high failure rate can be attributed to biofouling-related obstructions and infections. Previously, we developed flexible polyimide-based magnetic microactuators to remove obstructions formed on hydrocephalus shunts. To test the long-term …

Developing Strategies For Anatomical Characterization Of Locus Coeruleus - Cortical Projections, Kendra Wang, Qiuyu Wu, Alexander A. Chubykin

The Summer Undergraduate Research Fellowship (SURF) Symposium

The locus coeruleus (LC) is a small noradrenergic nucleus located in the midbrain that releases the neurotransmitter norepinephrine to diverse brain regions. Through release of norepinephrine, the LC plays a central role in modulating numerous physiological functions including attention, arousal, and mood and behavior. Although the LC projects to many brain region, there is limited information about the organization and the afferent projections to the LC that modulates its activity. The goal of this study was to characterize the anatomical projections between LC and cortical areas using a variety of different experimental techniques, including survival brain surgery, stereotaxic injections of …

Virtual V1sion: A Collaborative Coding Project, Cheryl Olman

MODVIS Workshop

Virtual V1sion is a new idea for fostering modeling collaborations and data sharing. While still in its infancy, the ultimate goal is a website that hosts repositories for (1) interchangeable model elements, (2) datasets that can be fit/predicted by those models, and (3) educational modules that explain the background for both the models and the datasets. The scope of the modeling is limited to predictions of V1 responses, although not all computations represented by model elements in Virtual V1sion are required to be V1-intrinsic: a goal of the project is to provide a framework in which predictions for modulation by …

Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd

MODVIS Workshop

Lightness matching data from disk-annulus experiments has the form of a parabolic (2^nd-order polynomial) function when matches are plotted against annulus luminance on log-log axes. Rudd (2010) has proposed a computational cortical model to account for this fact and has subsequently (Rudd, 2013, 2014, 2015) extended the model to explain data from other lightness paradigms, including staircase-Gelb and luminance gradient illusions (Galmonte, Soranzo, Rudd, & Agostini, 2015). Here, I re-analyze parametric lightness matching data from disk-annulus experiments by Rudd and Zemach (2007) and Rudd (2010) for the purpose of further testing the model and to try to constrain …

Failure Of Surface Color Cues Under Natural Changes In Lighting, David H. Foster, Iván Marín-Franch

MODVIS Workshop

Color allows us to effortlessly discriminate and identify surfaces and objects by their reflected light. Although the reflected spectrum changes with the illumination spectrum, cone photoreceptor signals can be transformed to give useful cues for surface color. But what happens when both the spectrum and the geometry of the illumination change, as with lighting from the sun and sky? Is it possible, as a matter of principle, to obtain reliable cues by processing cone signals alone? This question was addressed here by estimating the information provided by cone signals from time-lapse hyperspectral radiance images of five outdoor scenes under natural …

Modeling The Joint Distribution Of Scene Events At An Edge, James Elder, Ying Li

MODVIS Workshop

Edges in an image arise from discontinuities in scene variables, namely reflectance (R), illumination (I), depth (D) and surface orientation (O). Prior studies on edge classification have viewed it as a binary classification problem: each edge is assumed to arise from one of two disjoint categories (e.g., depth or not depth, shadow or not shadow). Here we suggest an alternate view in which an edge may signal discontinuities in any combination of the scene variables (RIDO). To explore this model, we had 4 trained observers label one randomly selected edge in each of 1,000 randomly selected images drawn from the …

Identifying Falsifiable Predictions Of The Divisive Normalization Model Of V1 Neurons, Tadamasa Sawada, Alexander A. Petrov

MODVIS Workshop

The divisive normalization model (DNM, Heeger, 1992) accounts successfully for a wide range of phenomena observed in single-cell physiological recordings from neurons in primary visual cortex (V1). The DNM has adjustable parameters to accommodate the diversity of V1 neurons, and is quite flexible. At the same time, in order to be falsifiable, the model must be rigid enough to rule out some possible data patterns. In this study, we discuss whether the DNM predicts any physiological result of the V1 neurons based on mathematical analysis and computational simulations. We identified some falsifiable predictions of the DNM. The main idea is …

Modelling Response Properties Across The Orientation Map In Visual Cortex, Erin M. Koch, Jianzhong Jin, Jose-Manuel Alonso, Qasim Zaidi

MODVIS Workshop

Stimulus orientation in the primary visual cortex of primates and carnivores is mapped as iso-orientation domains radiating from pinwheel centers, where orientation preferences of neighboring cells change circularly. Whether this orientation map has a function is debated, because many mammals, such as rodents, do not have such maps. Here we model our physiological results that two fundamental properties of visual cortical responses, contrast saturation and cross-orientation suppression, are stronger within iso-orientation domains than at pinwheel centers. Our model expands on a standard thalamic model of cross orientation suppression, and explains differences between orientation domains by intra-cortical excitation (not normalization) from …

Derivatives And Inverse Of A Linear-Nonlinear Multi-Layer Spatial Vision Model, Borja Galan, Marina Martinez-Garcia, Praveen Cyriac, Thomas Batard, Marcelo Bertalmio, Jesus Malo

MODVIS Workshop

Analyzing the mathematical properties of perceptually meaningful linear-nonlinear transforms is interesting because this computation is at the core of many vision models. Here we make such analysis in detail using a specific model [Malo & Simoncelli, SPIE Human Vision Electr. Imag. 2015] which is illustrative because it consists of a cascade of standard linear-nonlinear modules. The interest of the analytic results and the numerical methods involved transcend the particular model because of the ubiquity of the linear-nonlinear structure.

Here we extend [Malo&Simoncelli 15] by considering 4 layers: (1) linear spectral integration and nonlinear brightness response, (2) definition of local contrast …

Towards A Functional Explanation Of The Connectivity Lgn - V1, Marina Martinez-Garcia, Borja Galan, Luis M. Martinez, Jesus Malo

MODVIS Workshop

The principles behind the connectivity between LGN and V1 are not well understood. Models have to explain two basic experimental trends: (i) the combination of thalamic responses is local and it gives rise to a variety of oriented Gabor-like receptive felds in V1 [1], and (ii) these filters are spatially organized in orientation maps [2]. Competing explanations of orientation maps use purely geometrical arguments such as optimal wiring or packing from LGN [3-5], but they make no explicit reference to visual function. On the other hand, explanations based on func- tional arguments such as maximum information transference (infomax) [6,7] usually …

Towards A Unified Model Of Classical And Extra-Classical Receptive Fields, David A. Mély, Thomas Serre

MODVIS Workshop

One of the major goals in neuroscience is to understand how the cortex processes information. A substantial effort has thus gone into mapping classical receptive fields (cRF) across areas of the visual cortex and characterizing input-output relationships through linear-nonlinear response functions. Recently, there has been a lot of interest in mapping the extra-classical receptive field (extra-cRF) as well, by using contextual stimuli. The extra-cRF is a region outside the cRF that modulates a cell’s response but that is incapable of driving it on its own. However, existing models typically focus on one particular visual modality (form, motion, disparity or color), …

How Deep Is The Feature Analysis Underlying Rapid Visual Categorization?, Sven Eberhardt, Jonah Cader, Thomas Serre

MODVIS Workshop

Rapid categorization paradigms have a long history in experimental psychology: Characterized by short presentation times and fast behavioral responses, these tasks highlight both the speed and ease with which our visual system processes natural object categories. Previous studies have shown that feed-forward hierarchical models of the visual cortex provide a good fit to human visual decisions. At the same time, recent work has demonstrated significant gains in object recognition accuracy with increasingly deep hierarchical architectures: From AlexNet to VGG to Microsoft CNTK – the field of computer vision has championed both depth and accuracy. But it is unclear how well …

Spatial Synaptic Growth And Removal For Learning Individual Receptive Field Structures, Michael Teichmann, Fred H. Hamker

MODVIS Workshop

One challenge in creating neural models of the visual system is the appropriate definition of the connectivity. The modeler constrains the results with its definition. Unfortunately, there is often just insufficient information about connection sizes available, e.g. for deeper layer or different neuron types like interneurons. Hence, a mechanism refining the connection structure based on the learnings would be appreciated.

Such mechanism can be found in the human brain by structural plasticity. That is, the formation and removal of synapses. For our model, we exploit that synaptic connections are likely to be formed in the proximity of other synapses and …

A Geometric Approach To Sparse Coding Yields Insight Into Nonlinear Responses, Kedarnath Vilankar, James Golden, David Field

MODVIS Workshop

In artificial and biological networks, it is a common accepted practice to describe a neurons (biological or artificial) response properties by a two-dimensional feature map (receptive field). However, real neurons have nonlinear response properties which are not represented by their receptive fields. The efficient coding mechanisms such as sparse coding network or ICA, learn the response properties of V1 neurons from natural images using neural networks. These networks learn the receptive fields which are similar to the receptive fields of V1 neurons. These networks also produces some of the nonlinearities (such as end-stopping and non-classical surround effect), which are exhibited …