Author: Carlos A. M. NogueiraPublisher:
ISBN:
Category : Motion perception (Vision)
Languages : en
Pages : 196
Book Description
A Neural Network Model of Visual Motion Perception
Author: Carlos A. M. NogueiraPublisher:
ISBN:
Category : Motion perception (Vision)
Languages : en
Pages : 196
Book Description
Cortical Neural Network Models of Visual Motion Perception for Decision-Making and Reactive Navigation
Author: Michael BeyelerPublisher:
ISBN: 9781339830193
Category :
Languages : en
Pages : 212
Book Description
Animals use vision to traverse novel cluttered environments with apparent ease. Evidence suggests that the mammalian brain integrates visual motion cues across a number of remote but interconnected brain regions that make up a visual motion pathway. Although much is known about the neural circuitry that is concerned with motion perception in the Primary Visual Cortex (V1) and the Middle Temporal area (MT), little is known about how relevant perceptual variables might be represented in higher-order areas of the motion pathway, and how neural activity in these areas might relate to the behavioral dynamics of locomotion.The main goal of this dissertation is to investigate the computational principles that the mammalian brain might be using to organize low-level motion signals into distributed representations of perceptual variables, and how neural activity in the motion pathway might mediate behavior in reactive navigation tasks. I first investigated how the aperture problem, a fundamental conceptual challenge encountered by all low-level motion systems, can be solved in a spiking neural network model of V1 and MT (consisting of 153,216 neurons and 40 million synapses), relying solely on dynamics and properties gleaned from known electrophysiological and neuroanatomical evidence, and how this neural activity might influence perceptual decision-making. Second, when used with a physical robot performing a reactive navigation task in the real world, I found that the model produced behavioral trajectories that closely matched human psychophysics data. Essential to the success of these studies were software implementations that could execute in real time, which are freely and openly available to the community. Third, using ideas from the efficient-coding and free-energy principles, I demonstrated that a variety of response properties of neurons in the dorsal sub-region of the Medial Superior Temporal area (MSTd) area could be derived from MT-like input features. This finding suggests that response properties such as 3D translation and rotation selectivity, complex motion perception, and heading selectivity might simply be a by-product of MSTd neurons performing dimensionality reduction on their inputs. The hope is that these studies will not only further our understanding of how the brain works, but also lead to novel algorithms and brain-inspired robots capable of outperforming current artificial systems.
A Digital Neural Network Model of Motion Perception
Author: Jonathan Brian RogersAnalog VLSI Circuits for the Perception of Visual Motion
Author: Alan A. StockerPublisher: John Wiley & Sons
ISBN: 0470034882
Category : Technology & Engineering
Languages : en
Pages : 242
Book Description
Although it is now possible to integrate many millions of transistors on a single chip, traditional digital circuit technology is now reaching its limits, facing problems of cost and technical efficiency when scaled down to ever-smaller feature sizes. The analysis of biological neural systems, especially for visual processing, has allowed engineers to better understand how complex networks can effectively process large amounts of information, whilst dealing with difficult computational challenges. Analog and parallel processing are key characteristics of biological neural networks. Analog VLSI circuits using the same features can therefore be developed to emulate brain-style processing. Using standard CMOS technology, they can be cheaply manufactured, permitting efficient industrial and consumer applications in robotics and mobile electronics. This book explores the theory, design and implementation of analog VLSI circuits, inspired by visual motion processing in biological neural networks. Using a novel approach pioneered by the author himself, Stocker explains in detail the construction of a series of electronic chips, providing the reader with a valuable practical insight into the technology. Analog VLSI Circuits for the Perception of Visual Motion: analyses the computational problems in visual motion perception; examines the issue of optimization in analog networks through high level processes such as motion segmentation and selective attention; demonstrates network implementation in analog VLSI CMOS technology to provide computationally efficient devices; sets out measurements of final hardware implementation; illustrates the similarities of the presented circuits with the human visual motion perception system; includes an accompanying website with video clips of circuits under real-time visual conditions and additional supplementary material. With a complete review of all existing neuromorphic analog VLSI systems for visual motion sensing, Analog VLSI Circuits for the Perception of Visual Motion is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics and computational neuroscience. It will also be useful for researchers, professionals, and electronics engineers working in the field.
Dynamic Neural Field Theory for Motion Perception
Author: Martin A. GiesePublisher: Springer Science & Business Media
ISBN: 1461555817
Category : Science
Languages : en
Pages : 259
Book Description
Dynamic Neural Field Theory for Motion Perception provides a new theoretical framework that permits a systematic analysis of the dynamic properties of motion perception. This framework uses dynamic neural fields as a key mathematical concept. The author demonstrates how neural fields can be applied for the analysis of perceptual phenomena and its underlying neural processes. Also, similar principles form a basis for the design of computer vision systems as well as the design of artificially behaving systems. The book discusses in detail the application of this theoretical approach to motion perception and will be of great interest to researchers in vision science, psychophysics, and biological visual systems.
Dynamics of Visual Motion Processing
Author: Guillaume S. MassonPublisher: Springer Science & Business Media
ISBN: 1441907815
Category : Medical
Languages : en
Pages : 362
Book Description
Motion processing is an essential piece of the complex brain machinery that allows us to reconstruct the 3D layout of objects in the environment, to break camouflage, to perform scene segmentation, to estimate the ego movement, and to control our action. Although motion perception and its neural basis have been a topic of intensive research and modeling the last two decades, recent experimental evidences have stressed the dynamical aspects of motion integration and segmentation. This book presents the most recent approaches that have changed our view of biological motion processing. These new experimental evidences call for new models emphasizing the collective dynamics of large population of neurons rather than the properties of separate individual filters. Chapters will stress how the dynamics of motion processing can be used as a general approach to understand the brain dynamics itself.
Neural Computation of Pattern Motion
Author: Margaret Euphrasia SerenoPublisher: MIT Press
ISBN: 9780262193290
Category : Medical
Languages : en
Pages : 196
Book Description
This book describes a neurally based model, implemented as a connectionist network, of how the aperture problem is solved.
Vision
Author: Jeanny HraultPublisher: World Scientific
ISBN: 9814273694
Category : Computers
Languages : en
Pages : 308
Book Description
At the fascinating frontiers of neurobiology, mathematics and psychophysics, this book addresses the problem of human and computer vision on the basis of cognitive modeling. After recalling the physics of light and its transformation through media and optics, Hrault presents the principles of the primate's visual system in terms of anatomy and functionality. Then, the neuronal circuitry of the retina is analyzed in terms of spatio?temporal filtering. This basic model is extended to the concept of neuromorphic circuits for motion processing and to the processing of color in the retina. For more in-depth studies, the adaptive non-linear properties of the photoreceptors and of ganglion cells are addressed, exhibiting all the power of the retinal pre-processing of images as a system of information cleaning suitable for further cortical processing. As a target of retinal information, the primary visual area is presented as a bank of filters able to extract valuable descriptors of images, suitable for categorization and recognition and also for local information extraction such as saliency and perspective. All along the book, many comparisons between the models and human perception are discussed as well as detailed applications to computer vision.
Neural networks for perception
Author: Harry WechslerPublisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 550
Book Description
Neural Networks for Perception
Author: Harry WechslerPublisher: Academic Press
ISBN: 1483260259
Category : Computers
Languages : en
Pages : 543
Book Description
Neural Networks for Perception, Volume 1: Human and Machine Perception focuses on models for understanding human perception in terms of distributed computation and examples of PDP models for machine perception. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The book is organized into two parts. The first part focuses on human perception. Topics on network model of object recognition in human vision, the self-organization of functional architecture in the cerebral cortex, and the structure and interpretation of neuronal codes in the visual system are detailed under this part. Part two covers the relevance of neural networks for machine perception. Subjects considered under this section include the multi-dimensional linear lattice for Fourier and Gabor transforms, multiple- scale Gaussian filtering, and edge detection; aspects of invariant pattern and object recognition; and neural network for motion processing. Neuroscientists, computer scientists, engineers, and researchers in artificial intelligence will find the book useful.