Author: Jonathan Brian RogersPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
A Digital Neural Network Model of Motion Perception
Author: Jonathan Brian RogersA Neural Network Model of Visual Motion Perception
Author: Carlos A. M. NogueiraPublisher:
ISBN:
Category : Motion perception (Vision)
Languages : en
Pages : 196
Book Description
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.
Analog 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.
Gestalt Perception of Biological Motion with a Generative Artificial Neural Network Model
Author: Mahdi SadeghiCortical 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.
Deep Learning for Robot Perception and Cognition
Author: Alexandros IosifidisPublisher: Academic Press
ISBN: 0323885721
Category : Computers
Languages : en
Pages : 638
Book Description
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Space-Time Geometries for Motion and Perception in the Brain and the Arts
Author: Tamar FlashPublisher: Springer Nature
ISBN: 3030572277
Category : Mathematics
Languages : en
Pages : 265
Book Description
This book is based on a two-day symposium at the Paris Institute of Advanced Study titled "space-time geometries and movement in the brain and the arts". It includes over 20 chapters written by the leading scientists and artists who presented their related research studies at the symposium and includes six sections; the first three focus on space-time geometries in perception, action and memory while the last three focus on specific artistic domains: drawing and painting, dance, music, digital arts and robotics. The book is accompanied by a dedicated webpage including related images and videos. There is an ever-growing interest in the topics covered by this book. Space and time are of fundamental importance for our understanding of human perception, action, memory and cognition, and are entities which are equally important in physics, biology, neuroscience and psychology. Highly prominent scientists and mathematicians have expressed their belief that our bodies and minds shape the ways we perceive space and time and the physical laws we formulate. Understanding how the brain perceives motion and generates -bodily movements is of great significance. There is also growing interest in studying how space, time and movement subserve artistic creations in different artistic modalities (e.g., fine arts, digital and performing arts and music). This interest is inspired by the idea that artists make intuitive use of the principles and simplifying strategies used by the brain in movement generation and perception. Building upon new understanding of the spatio-temporal geometries subserving movement generation and perception by the brain we can start exploring how artists make use of such neuro --geometrical and neuro-dynamic representations in order to express artistic concepts and emotionally affect the human observers and listeners. Scientists have also started formulating new ideas of how aesthetic judgements emerge from the principles and brain mechanisms subserving motor control and motion perception. Covering novel and multidisciplinary topics, this advanced book will be of interest to neuroscientists, behavioral scientists, artificial intelligence and robotics experts, students and artists.
Self-organizing Neural Networks for Perception of Visual Motion
Author: Jonathan A. MarshallPublisher:
ISBN:
Category : Neural circuitry
Languages : en
Pages : 107
Book Description
Neural Models of Motion Perception
Author: Stephen GrossbergPublisher:
ISBN:
Category : Motion perception (Vision)
Languages : en
Pages : 10
Book Description