Pii: S0893-6080(98)00107-5
نویسندگان
چکیده
The barn owl is a nocturnal predator that is able to capture mice in complete darkness using only sound to localize prey. Two binaural cues are used by the barn owl to determine the spatial position of a sound source: differences in the time of arrival of sounds at the two ears for the azimuth (interaural time differences (ITDs)) and differences in their amplitude for the elevation (interaural level differences (ILDs)). Neurophysiological investigations have revealed that two different neural pathways starting from the cochlea seem to be specialized for processing ITDs and ILDs. Much evidence suggests that in the barn owl the localization of the azimuth is based on a cross-correlation-like treatment of the auditory inputs at the two ears. In particular, in the external nucleus of the inferior colliculus (ICx), where cells are activated by specific values of ITD, neural activation has been recently observed to be dependent on some measure of the level of cross-correlation between the input auditory signals. However, it has also been observed that these neurons are less sensitive to noise than predicted by direct binaural cross-correlation. The mechanisms underlying such signal-to-noise improvement are not known. In this paper, by focusing on a model of the barn owl’s neural pathway to the optic tectum dedicated to the localization of the azimuth, we study the mechanisms by which the ITD tuning of ICx units is achieved. By means of analytical examinations and computer simulations, we show that strong analogies exist between the process by which the barn owl evaluates the azimuth of a sound source and the generalized cross-correlation algorithm, one of the most robust methods for the estimate of time delays. q 1999 Elsevier Science Ltd. All rights reserved.
منابع مشابه
Self-organized hierarchical structure in a plastic network of chaotic units
Formation of a layered structure is studied in a globally coupled map of chaotic units with a plastic coupling strength that changes depending on the states of units globally and an external input. In the parameter region characterized by weakly chaotic and desynchronized dynamics, units spontaneously form a hierarchical structure due to the influence of the input. This hierarchical structure i...
متن کاملExplanation of the "virtual input" phenomenon
We write this letter to comment on the "virtual input" phenomenon reported by Thaler (Neural Networks, 8(1) (1995) 55-65). The author attributed the phenomenon to the network's ability to perform pattern classification and completion, and reported that pruning probability affects the number of virtual inputs observed. Our independent study of Thaler's results, however, reveals a simpler explana...
متن کاملSpace-filling curves and Kolmogorov superposition-based neural networks
Kolmogorov superpositions and Hecht-Nielsen's neural network based on them are dimension reducing. This dimension reduction can be understood in terms of space-filling curves that characterize Kolmogorov's functions, and the subject of this paper is the construction of such a curve. We construct a space-filling curve with Lebesgue measure 1 in the unit square, [0,1]2, with approximating curves ...
متن کاملPattern completion through phase coding in population neurodynamics
This article presents an alternative phase coding mechanism for Freeman's KIII model of population neurodynamics. Motivated by experimental evidence that supports the existence of a neural code based on synchronous oscillations, we propose an analogy between synchronization in neural populations and phase locking in KIII channels. An efficient method is proposed to extract phase differences acr...
متن کاملInformation storage capacity of incompletely connected associative memories
In this paper, the memory capacity of incompletely connected associative memories is investigated. First, the capacity is derived for memories with fixed parameters. Optimization of the parameters yields a maximum capacity between 0.53 and 0.69 for hetero-association and half of it for autoassociation improving previously reported results. The maximum capacity grows with increasing connectivity...
متن کاملParallel and robust skeletonization built on self-organizing elements
A massively parallel neural architecture is suggested for the approximate computation of the skeleton of a planar shape. Numerical examples demonstrate the robustness of the method. The architecture is constructed from self-organizing elements that allow the extension of the concept of skeletonization to areas remote to image processing.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1999