The brain's cerebral cortex decomposes visual images into information about oriented edges, direction and velocity information, and color. How does the cortex decompose perceived sounds? A reverse correlation technique demonstrates that neurons in the primary auditory cortex of the awake primate have complex patterns of sound-feature selectivity that indicate sensitivity to stimulus edges in frequency or in time, stimulus transitions in frequency or intensity, and feature conjunctions. This allows the creation of classes of stimuli matched to the processing characteristics of auditory cortical neurons. Stimuli designed for a particular neuron's preferred feature pattern can drive that neuron with higher sustained firing rates than have typically been recorded with simple stimuli. These data suggest that the cortex decomposes an auditory scene into component parts using a feature-processing system reminiscent of that used for the cortical decomposition of visual images.

Tactile pattern recognition depends on form and texture perception. A principal dimension of texture perception is roughness, the neural coding of which was the focus of this study. Previous studies have shown that perceived roughness is not based on neural activity in the Pacinian or cutaneous slowly adapting type II (SAII) neural responses or on mean impulse rate or temporal patterning in the cutaneous slowly adapting type I (SAI) or rapidly adapting (RA) discharge evoked by a textured surface. However, those studies found very high correlations between roughness scaling by humans and measures of spatial variation in SAI and RA firing rates. The present study used textured surfaces composed of dots of varying height (280-620 m) and diameter (0.25-2.5 mm) in psychophysical and neurophysiological experiments. RA responses were affected least by the range of dot diameters and heights that produced the widest variation in perceived roughness, and these responses could not account for the psychophysical data. In contrast, spatial variation in SAI impulse rate was correlated closely with perceived roughness over the whole stimulus range, and a single measure of SAI spatial variation accounts for the psychophysical data in this (0.974 correlation) and two previous studies. Analyses based on the possibility that perceived roughness depends on both afferent types suggest that if the RA response plays a role in roughness perception, it is one of mild inhibition. These data reinforce the hypothesis that SAI afferents are mainly responsible for information about form and texture whereas RA afferents are mainly responsible for information about flutter, slip, and motion across the skin surface. Key words: pattern recognition; texture; roughness; mechanoreceptor; somatosensory; neurophysiology; psychophysics; rhesusThe issue addressed in this paper is the neural code underlying tactile roughness perception. The possibilities include intensive, temporal, or spatial coding mechanisms in any of the four cutaneous mechanoreceptive afferent populations that innervate the hand. Texture perception and its neural mechanisms have been studied extensively (Lederman, 1974;LaMotte, 1977;Lederman et al., 1982;Johnson, 1983;Sathian et al., 1989;Phillips et al., 1992;Phillips and Matthews, 1993;Burton and Sinclair, 1994;Johnson and Hsiao, 1994). The earliest studies provided quantitative characterization of roughness perception and demonstrated that all cutaneous mechanoreceptive afferents except cutaneous slowly adapting type II (SAII) afferents (Phillips et al., 1992) respond vigorously to textured surfaces and could provide a basis for roughness perception. T wo combined psychophysical and neurophysiological studies (Connor et al., 1990;Connor and Johnson, 1992) have since narrowed the coding possibilities to those based on spatial variation in cutaneous slowly adapting type I (SAI) and rapidly adapting (R A) impulse rates. The study reported here was designed to investigate the relative contributions of SAI and R A afferents to roughn...

This paper concerns the characterization of performance and perceptual learning of somatosensory interval discrimination. The purposes of this study were to define (1) the performance characteristics for interval discrimination in the somatosensory system by naive adult humans, (2) the normal capacities for improvement in somatosensory interval discrimination, and (3) the extent of generalization of interval discrimination learning. In a two-alternative forced choice procedure, subjects were presented with two pairs of vibratory pulses. One pair was separated in time by a fixed base interval; a second pair was separated by a target interval that was always longer than the base interval. Subjects indicated which pair was separated by the target interval. The length of the target interval was varied adaptively to determine discrimination thresholds. After initial determination of naive abilities, subjects were trained for 900 trials per day at base intervals of either 75 or 125 msec for 10-15 d. Significant improvements in thresholds resulted from training. Learning at the trained base interval generalized completely across untrained skin locations on the trained hand and to the corresponding untrained skin location in the contralateral hand. The learning partially generalized to untrained base intervals similar to the trained one, but not to more distant base intervals. Learning with somatosensory stimuli generalized to auditory stimuli presented at comparable base intervals. These results demonstrate temporal specificity in somatosensory interval discrimination learning that generalizes across skin location, hemisphere, and modality.

Primates engage in auditory behaviors under a broad range of signal-to-noise conditions. In this study, optimal linear receptive fields were measured in alert primate primary auditory cortex (A1) in response to stimuli that vary in spectrotemporal density. As density increased, A1 excitatory receptive fields systematically changed. Receptive field sensitivity, expressed as the expected change in firing rate after a tone pip onset, decreased by an order of magnitude. Spectral selectivity more than doubled. Inhibitory subfields, which were rarely recorded at low sound densities, emerged at higher sound densities. The ratio of excitatory to inhibitory population strength changed from 14.4:1 to 1.4:1. At low sound densities, the sound associated with the evocation of an action potential from an A1 neuron was broad in spectrum and time. At high sound densities, a spike-evoking sound was more likely to be a spectral or temporal edge and was narrower in time and frequency range. Receptive fields were used to predict responses to a novel high-noise-density stimulus. The predictions were highly correlated with the actual responses to the 2-s complex sound excerpt. The structure of prediction failures revealed that neurons with prominent inhibitory fields had relatively poor linear predictions. Further, the finding that stochastic variance is limiting in prediction even after averaging 150 repetitions means that high-fidelity representations of simple sounds in A1 must be distributed over at least hundreds of neurons. Auditory context alters A1 responses across multiple parameter spaces; this presents a challenge for reconstructing neural codes.