Computational Neuroscience Commons^™

Incorporating Action Information Into Computational Models Of The Human Visual System, Justin Zhou

Undergraduate Student Research Internships Conference

Deep convolutional neural networks (DCNNs) have been used to model the ventral visual stream. However, there have been relatively few computational models of the dorsal visual stream, preventing a wholistic understanding of the human visual system. Additionally, current DCNN models of the ventral stream have shortcomings (such as an over-reliance on texture data) which can be ameliorated by incorporating dorsal stream information. The current study aims to investigate two questions: 1) does incorporating action information improve computational models of the ventral visual system? 2) how do the ventral and dorsal streams influence each other during development?

Three models will be …

Finding Any Waldo: Zero-Shot Invariant And Efficient Visual Search, Gabriel Kreiman, Mengmi Zhang

MODVIS Workshop

Visual search constitutes a ubiquitous challenge in natural vision, including daily tasks such as finding a friend in a crowd or searching for a car in a parking lot. Visual search must fulfill four key properties: selectivity (to distinguish the target from distractors in a cluttered scene), invariance (to localize the target despite changes in its rotation, scale, illumination, and even searching for generic object categories), speed (to efficiently localize the target without exhaustive sampling), and generalization (to search for any object, even ones that we have had minimal or no experience with). Here we propose a computational model that …

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

MODVIS Workshop

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 …

Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker

MODVIS Workshop

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 …

The Possible Connection Of Gamma Oscillation And 3-D Object Representation, Thien N. Vu

Summer Research

We process and encode for different features of a particular object (shape, color, texture, etc.) in distinct areas of the brain. How we bind these attributes together into a unified perception of an object is unknown. Past research suggests that synchronized activity between brain areas, particularly induced gamma activity (~ 40 Hz), may account for this binding process and the basis of our conscious perceptual experience, specifically through object representation. In this study, participants were asked to look at a series of 2-D pictures of cars from distinctive rotations (0⁰, 90⁰, 180⁰) and were …