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The Journal of Neurophysiology Vol. 80 No. 2 August 1998,
pp. 863-881
Copyright ©1998 The American Physiological Society
1 Department of Neuroscience and 2 Department of Psychology, University of Florida, Gainesville, Florida 32610; and 3 Kresge Hearing Research Institute, University of Michigan, Ann Arbor, Michigan 48109-0506
Middlebrooks, John C., Li Xu, Ann Clock Eddins, and David M. Green. Codes for sound-source location in nontonopic auditor cortex. J. Neurophysiol. 80: 863-881, 1998. We evaluated two hypothetical codes for sound-source location in the auditory cortex. The topographical code assumed that single neurons are selective for particular locations and that sound-source locations are coded by the cortical location of small populations of maximally activated neurons. The distributed code assumed that the responses of individual neurons can carry information about locations throughout 360° of azimuth and that accurate sound localization derives from information that is distributed across large populations of such panoramic neurons. We recorded from single units in the anterior ectosylvian sulcus area (area AES) and in area A2 of 
-chloralose-anesthetized cats. Results obtained in the two areas were essentially equivalent. Noise bursts were presented from loudspeakers spaced in 20° intervals of azimuth throughout 360° of the horizontal plane. Spike counts of the majority of units were modulated >50% by changes in sound-source azimuth. Nevertheless, sound-source locations that produced greater than half-maximal spike counts often spanned >180° of azimuth. The spatial selectivity of units tended to broaden and, often, to shift in azimuth as sound pressure levels (SPLs) were increased to a moderate level. We sometimes saw systematic changes in spatial tuning along segments of electrode tracks as long as 1.5 mm but such progressions were not evident at higher sound levels. Moderate-level sounds presented anywhere in the contralateral hemifield produced greater than half-maximal activation of nearly all units. These results are not consistent with the hypothesis of a topographic code. We used an artificial-neural-network algorithm to recognize spike patterns and, thereby, infer the locations of sound sources. Network input consisted of spike density functions formed by averages of responses to eight stimulus repetitions. Information carried in the responses of single units permitted reasonable estimates of sound-source locations throughout 360° of azimuth. The most accurate units exhibited median errors in localization of <25°, meaning that the network output fell within 25° of the correct location on half of the trials. Spike patterns tended to vary with stimulus SPL, but level-invariant features of patterns permitted estimates of locations of sound sources that varied through 20-dB ranges. Sound localization based on spike patterns that preserved details of spike timing consistently was more accurate than localization based on spike counts alone. These results support the hypothesis that sound-source locations are represented by a distributed code and that individual neurons are, in effect, panoramic localizers.
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