We examined the coding of sound-source location by ensembles of neurons in the auditory cortex. Broadband noise bursts were presented from loudspeakers throughout 360degrees in the horizontal plane. Sound levels varied from 20 to 40 dB above neural thresholds. We recorded temporal spike patterns simultaneously at 16 recording sites in area A2 of [alpha]-chloralose-anesthetized cats. Spike patterns of individual units varied in spike counts and in spike timing as a function of sound-source location. Ensembles of up to 19 units recorded simultaneously demonstrated additional location sensitivity in the form of relative spike counts and relative spike timing among neurons. We used an artificial neural network (ANN) algorithm to recognize ensemble spike patterns and, thereby, to infer the locations of sound sources. The ANN could estimate stimulus locations based on ensemble responses to single-stimulus presentations. Median errors (MEs) averaged 49.2 +/- 11.9degrees (mean +/- SD; n = 34; chance level, 90degrees). The ANN maintained better-than-chance performance even when input spike patterns were expressed as relative spike counts across units (i.e., no information available from absolute spike counts of individual units; ME, 63.0 +/- 11.8degrees) or when spike latencies were represented as time relative to the first spike for each trial (i.e., no external time reference available; ME, 54.3 +/- 12.4degrees). The ANN performance improved monotonically as the sizes of ensemble patterns were increased by combining patterns across the entire unit sample. The performance by ensembles of 128 units approached the level of localization performance of behaving cats.
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