Bio-inspired networks of visual sensors, neurons, and oscillators

作     者：Ghosh, Bijoy K. Polpitiya, Ashoka D. Wang, Wenxue 

作者机构：Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA Pacific NW Natl Lab Richland WA 99352 USA 

出 版 物：《PROCEEDINGS OF THE IEEE》 (电气与电子工程师学会会报)

年 卷 期：2007年第95卷第1期

页      面：188-214页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Science Foundation  NSF  (ECS-0323693  EIA-0218186) 

主　　题：cortical waves eye movement formation sensing gaze control Hebbian and anti-Hebbian adaptation Kuramoto model Listing's law localization neural network oscillator network place cells sensor network sparse coding theta phase precession 

摘      要：Animals routinely rely on their eyes to localize fixed and moving targets. Such a localization process might include prediction of future target location, recalling a sequence of previously visited places or, for the motor control circuit, actuating a successful movement. Typically, target localization is carried out by fusing images from two eyes, in the case of binocular vision, wherein the challenge is to have the images calibrated before fusion. in the field of machine vision, a typical problem of interest is to localize the position and orientation of a network of mobile cameras (sensor network) that are distributed in space and are simultaneously tracking a target. Inspired by the animal visual circuit, we study the problem of binocular image fusion for the purpose of. localizing an unknown target in space. Guided by the dynamics of eye rotation, we introduce control strategies that could be used to build machines with multiple sensors. In particular, we address the problem of how a group of visual sensors can be optimally controlled in a formation. we also address how images from multiple sensors are encoded using a set of basis functions, choosing a larger than minimum number of basis functions so that the resulting code that represents the image is sparse. We address the problem of how a sparsely encoded visual data stream is internally represented by a pattern of neural activity. in addition to the control mechanism, the synaptic interaction between cells is also subjected to adaptation that enables the activity waves to respond with greater sensitivity to visual input. We study how the rat hippocampal place cells are used to form a cognitive map of the environment so that the animal s location can be determined from its place cell activity. Finally, we study the problem of decoding location of moving targets from the neural activity wave in the cortex.