Auditory Frequency and Intensity Discrimination Explained Using a Cortical Population Rate Code

Oxenham AJ (2013) Auditory Frequency and Intensity Discrimination Explained Using a Cortical Population Rate Code. PLoS Comput Biol 9(11): e1003336. doi:10.1371/journal.pcbi.1003336 Auditory Frequency and Intensity Discrimination Explained Using a Cortical Population Rate Code Christophe Micheyl 0 Paul R. Schrater 0 Andrew J. Oxenham 0 Timothy D. Griffiths, Newcastle University Medical School, United Kingdom 0 1 Department of Psychology, University of Minnesota , Minneapolis , Minnesota, United States of America, 2 Department of Computer Science, University of Minnesota , Minneapolis , Minnesota, United States of America, 3 Department of Otolaryngology, University of Minnesota , Minneapolis, Minnesota , United States of America The nature of the neural codes for pitch and loudness, two basic auditory attributes, has been a key question in neuroscience for over century. A currently widespread view is that sound intensity (subjectively, loudness) is encoded in spike rates, whereas sound frequency (subjectively, pitch) is encoded in precise spike timing. Here, using informationtheoretic analyses, we show that the spike rates of a population of virtual neural units with frequency-tuning and spikecount correlation characteristics similar to those measured in the primary auditory cortex of primates, contain sufficient statistical information to account for the smallest frequency-discrimination thresholds measured in human listeners. The same population, and the same spike-rate code, can also account for the intensity-discrimination thresholds of humans. These results demonstrate the viability of a unified rate-based cortical population code for both sound frequency (pitch) and sound intensity (loudness), and thus suggest a resolution to a long-standing puzzle in auditory neuroscience. - The nature of the neural code for perception is a fundamental question in neuroscience [15]. In auditory neuroscience, the search for the neural code for pitchan essential perceptual attribute of sound classes such as music and speechhas attracted considerable interest [69]. Two main types of neural codes for pitch have been offered: timing codes, which rely on fine spiketiming information [10], and rate codes, which involve spike rates computed over relatively long time windowstypically, a few hundred milliseconds [11]. Timing codes can carry considerably more information than rate codes [12], and the spike times of auditory-nerve fibers have been found to contain more information than needed to account for human listeners ability to discriminate very small changes in frequency [11,13,14]. However, temporal coding degrades rapidly beyond the auditory nerve, making spike timing a less viable code at higher levels of neural processing. Indeed, in the primary auditory cortex, single units cannot precisely follow frequencies higher than a few hundred Hertz [1517] more than an order of magnitude below the upper limit of accurate pitch perception in humans [1820]. Although studies in non-human animals found no deficits in pure-tone intensity or frequency discrimination following bilateral ablation of auditory cortex, substantial deficits in pure-tone frequency (pitch) and intensity (loudness) discrimination have been observed in human patients with cortical lesions [21,22], suggesting that the auditory cortex plays an important role in those two perceptual abilities. It seems likely, therefore, that any timing code for frequency in the auditory nerve is transformed into a cortical rate-place code. However, it is not known whether the information contained in the spike counts of a population of cortical neurons is sufficient to account for the very fine frequency-discrimination thresholds of human listeners. A cortical rate-place code for frequency discrimination faces two major obstacles: relatively broad receptive fields [23], implying poor resolution of small frequency differences by single units, and correlated spike counts [24,25], which can severely limit the benefit of pooling information across multiple units [2629]. Here, we examine the properties of a population of virtual neurons with frequency-tuning and spike-count correlation characteristics similar to those measured in the primary auditory cortex of primates. We determine that statistically optimal decoding of the information contained in the spike rates of these neurons can account quantitatively for the remarkable ability of trained human listeners to discriminate sound frequency. In addition, we show that the same cortical population code is also consistent with psychophysical data concerning another fundamental auditory ability: intensity discrimination. These results demonstrate the viability of a cortical rate code for both frequency and intensity discrimination, thus providing a possible resolution for a longstanding puzzle in auditory neuroscience. Figure 1A shows frequency tuning curves (spike-rate versus stimulus frequency) for an array of virtual frequency-selective neurons with best frequencies (BFs) equally spaced on a logarithmic scale spanning a 1-octave range centered on 1 kHz. For illustration purposes, tuning curves are plotted for a small subset of units (n = 6) and a limited BF range, but the results A widely held view among auditory scientists is that the neural code for sound intensity (or loudness) involves temporally coarse spike-rate information, whereas the code for sound frequency (or pitch) requires more finegrained and precise spike timing information. One problem with this view is that neurons in auditory cortex do not produce precisely time-locked responses to higher frequencies within the pitch range, suggesting that a transformation to a rate code must occur. However, because cortical neurons exhibit relatively broad tuning to frequency and correlated spike counts, it is unclear whether a cortical population code based on spike rates alone can support the remarkably precise pitch-discrimination ability of humans. Here we show that a relatively small population of virtual neurons with frequency-tuning and spike-count correlation characteristics consistent with those of actual neurons in the primary auditory cortex of primates, can account for both the smallest frequency- and intensity-discrimination thresholds measured behaviorally in humans. These results suggest a resolution to a longstanding puzzle in auditory neuroscience. described below are based on a larger number of units (n = 1700) and a wider BF range (2 octaves). A key characteristic of neural tuning curves is their sharpness. A common measure of sharpness is the quality factor (Q), which is obtained by dividing the BF of the unit by a measure of tuning, in this case the width of the tuning curve at half of the peak spiking rate. The sharpness of the simulated units was adjusted to yield Q values consistent with those measured in the primary auditory cortex of primates, which have been found to equal 12 on average for sharply t (...truncated)