Neuroscience and Neurobiology Commons^™

The Road Towards Image-Computable Models Of Human Visual Grasp Planning, Guido Maiello, Lina K. Klein, Vivian C. Paulun, Katherine R. Storrs, Roland W. Fleming

MODVIS Workshop

No abstract provided.

A Feature-Based Model Of Visually Perceiving Deformable Objects, Vivian C. Paulun, Filipp Schmidt, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Effect Of Noise On Mutually Inhibiting Pyramidal Cells In Visual Cortex: Foundation Of Stochasticity In Bi-Stable Perception, Naoki Kogo, Felix Kern, Thomas Nowotny, Raymond Van Ee, Richard Van Wezel, Takeshi Aihara

MODVIS Workshop

Bi-stable perception has been an important tool to investigate how visual input is interpreted and how it reaches consciousness. To explain the mechanisms of this phenomenon, it has been assumed that a mutual inhibition circuit plays a key role. It is possible that this circuit functions to resolve ambiguity of input image by quickly shifting the balance of competing signals in response to conflicting features. Recently we established an in vitro neural recording system combined with computerized connections mediated by model neurons and synapses (“dynamic clamp” system). With this system, mutual inhibition circuit between two pyramidal cells from primary visual …

Visual Category Learning By Means Of Basal Ganglia, Fred H. Hamker, Francesc Villagrasa, Javier Baladron, Henning Schroll, Julien Vitay

MODVIS Workshop

No abstract provided.

Why Latent Representations In Convolutional Neural Networks Fall Outside Visual Space, Katerina Malakhova

MODVIS Workshop

It is common to compare properties of visual information processing by artificial neural networks and the primate visual system.

Some remarkable similarities were observed in the responses of neurons in IT cortex and units in higher layers of CNNs. Here I show that latent representations formed by weights in convolutional layers do not necessarily reflect visual domain. Instead they are strongly dependent on a choice of training set and cost function.

The most striking example is when an individual unit, which is highly selective to some members of a category is, nevertheless, inhibited by visually similar objects of the same …

Appropriate Kernels For Divisive Normalization Explained By Wilson-Cowan Equations, Jesus Malo, Marcelo Bertalmio

MODVIS Workshop

Cascades of standard Linear+NonLinear-Divisive Normalization transforms [Carandini&Heeger12] can be easily fitted using the appropriate formulation introduced in [Martinez17a] to reproduce the perception of image distortion in naturalistic environments. However, consistently with [Rust&Movshon05], training the model in naturalistic environments does not guarantee the prediction of well known phenomena illustrated by artificial stimuli. For example, the cascade of Divisive Normalizations fitted with image quality databases has to be modified to include a variety aspects of masking of simple patterns. Specifically, the standard Gaussian kernels of [Watson&Solomon97] have to be augmented with extra weights [Martinez17b]. These can be introduced ad-hoc using the intuition …

Model Investigation On Contribution Of Feedback In Distortion Induced Motion Adaptation, Siegfried Wahl, Selam Habtegiorgis, Christian Jarvers, Katharina Rifai, Heiko Neumann

MODVIS Workshop

Motion information is processed in a neural circuit formed by synaptic organization of feedforward (FF) and feedback (FB) connections between different cortical areas. However, the contribution of a recurrent FB information to adaptation process is not well explored. Here, a biologically plausible neural model that predicts motion adaptation aftereffect (MAE) induced by exposure to geometrically skewed natural image sequences is suggested. The model constitutes two stage recurrent motion processing within cortical areas V1 and MT [1]. It comprises FF excitatory, FB modulatory and lateral inhibitory connections, and a fast and a slow adaptive synapse in the FF and FB streams, …

A Model Of 1d And 2d Motion Processing In The Primate Brain, Alan Johnston

MODVIS Workshop

Velocity encoding in the primate brain can be modelled by a spatiotemporal gradient approach, with neurons characterized as spatio-temporal derivative operators (Johnston et al. 1999). This strategy works well for moving 1D spatial patterns, but it can produce systematic errors, as it can be overly influenced by the direction of the local spatial gradient of the image brightness. For 2D pattern it is possible to develop a similar spatio-temporal approach, in which the system solves a set of over-determined linear equations directly, to provide an estimate for the 2D image motion. However, in this case the matrix one needs to …

An Active Efficient Coding Model Of The Development Of Amblyopia, Samuel Eckmann, Lukas Klimmasch, Bertram Shi, Jochen Triesch

MODVIS Workshop

No abstract provided.

A Spatial Stochastic Model Of Ampar Trafficking And Subunit Dynamics, Tyler Vandyk, Matthew C. Pharris, Tamara L. Kinzer-Ursem

The Summer Undergraduate Research Fellowship (SURF) Symposium

In excitatory neurons, the ability of a synaptic connection to strengthen or weaken is known as synaptic plasticity and is thought to be the cellular basis for learning and memory. Understanding the mechanism of synaptic plasticity is an important step towards understanding and developing treatment methods for learning and memory disorders. A key molecular process in synaptic plasticity for mammalian glutamatergic neurons is the exocytosis (delivery to the synapse) of AMPA-type glutamate receptors (AMPARs). While the protein signaling pathways responsible for exocytosis have long been investigated with experimental methods, it remains unreasonable to study the system in its full complexity …

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral measures …

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 …

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.

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.

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.

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 …