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CORTICAL computational mapS FOR AUDITORY IMAGING

NEURAL NETWORKS 1990年第1期3卷 3-21页

作者： SUGA, N Washington University USA

The mustached bat emits complex biosonar signals (pulses) and listens to echoes for orientation and hunting flying insects. Different types of biosonar information are conveyed by different parameters characterizing pulse-echo pairs. For example, range information is conveyed by echo delay, while velocity information is carried by Doppler shift. At the auditory periphery, frequency is expressed by the anatomical location along the basilar membrane and also along the array of ganglion cells, while amplitude and time (duration of signals and interval between signals) are not expressed by anatomical locations, but by discharge rate and the temporal pattern of nerve discharges, respectively. In the auditory cortex, however, not only frequency but also other information-bearing parameters (IBPs) such as echo delay and Doppler shift are systematically expressed by anatomical locations. That is, the IBPs are mapped. These computational maps greatly depend upon subcortical signal processing. The subcortical auditory nuclei create delay lines and multipliers (or AND gates) for processing range (echo delay) information, and also create level-tolerant frequency tuning and multipliers (or AND gates) for processing velocity (Doppler shift) information. These multipliers are called FM-FM or CF/CF combination-sensitive neurons, respectively. Signal processing in the auditory system is parallel-hierarchical. The neurophysiological studies of the bat's auditory system provide an excellent data base for computational models.

关键词： Auditory imaging Bat Biosonar computational map Cross-correlation Delay line Multiplier Neural computation

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Field computation in natural and artificial intelligence

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INFORMATION SCIENCES 1999年第1-2期119卷 73-89页

作者： MacLennan, BJ Univ Tennessee Dept Comp Sci Knoxville TN 37916 USA

We review the concepts of field computation, a model of computation that processes information represented as spatially continuous arrangements of continuous data. We show that many processes in the brain are described usefully as field computation. Throughout we stress the connections between field computation and quantum mechanics, especially including the important role of information fields, which represent by Virtue of their form rather than their magnitude. We also show that field computation permits simultaneous nonlinear computation in linear superposition. (C) 1999 Elsevier Science Inc. All rights reserved.

关键词： field computation continuum computation analog computation quantum computer quantum computation information field information superposition diffusion continuous representation subsymbolic Hilbert space Bohm Hiley pribram active information computational map cortical map phase phase encoding convolution Gabor wavelet uncertainty principle coherent state quantum mechanics logon multiresolution direction field field computer radial basis function RBF coarse coding pragmatics pragmatic information quantum potential symbol discrete optical computing optical computer molecular computing molecular computer

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STEREO BOUNDARY FUSION BY CORTICAL COMPLEX CELLS - A SYSTEM OF mapS, FILTERS, AND FEEDBACK NETWORKS FOR MULTIPLEXING DISTRIBUTED DATA

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NEURAL NETWORKS 1989年第1期2卷 29-51页

作者： GROSSBERG, S MARSHALL, JA Center for Adaptive Systems Boston University USA

A neural network model of multiple-scale binocular fusion and rivalry in visual cortex is described and simulated on the computer. The model consists of three parts: a distributed spatial representation of binocular input patterns among simple cells that are organized into ocular dominance columns; an adaptive filter from simple cells to complex cells; and a nonlinear on-center off-surround shunting feedback network that joins together the complex cells. This data structure generates complex cell receptive fields which multiplex input position, orientation, spatial frequency, positional disparity, and orientational disparity, and which are insensitive to direction-of-contrast in the image. Multiple copies of this circuit are replicated in the model using receptive fields of different sizes. Within each such circuit, the simple cell and complex cell receptive field sizes covary. Together these circuits define a self-similar multiple-scale network. The self-similarity property across spatial scales enables the network to exhibit a size-disparity correlation, whereby simultaneous binocular fusion and rivalry can occur among the spatial scales corresponding to a given retinal region. It is shown that a laminar organization of the model interactions among the complex cells gives rise to conceptually simple growth rules for intercellular connections. The output patterns of the model complex cells are designed to feed into the model hypercomplex cells at the first competitive stage of a Boundary Contour System network, where they trigger a process of multiple-scale emergent binocular boundary segmentation. The modeling results are compared with psychophysical data about binocular fusion and rivalry, as well as with the cepstrum stereo model of Yeshurun and Schwartz. The results indicate that analogous self-similar multiple-scale neural networks may be used to carry out data fusion of many other types of spatially organized data structures.

关键词： Neural network Binocular vision Visual cortex Data fusion Multiplexing Binocular rivalry computational map Complex cells Ocular dominance columns Self-similarity Size-disparity correlation nonlinear feedback network

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