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UBC Theses and Dissertations

Complex tone processing in the primate brain : behavioral and single unit experiments Tomlinson, Roger William Ward


The mechanisms of complex tone processing important in pitch perception were investigated at the behavioral, neurophysiological and theoretical level in a nonhuman primate, the rhesus monkey (Macaca mulatto). Four rhesus monkeys were trained to press a button when the fundamental frequencies (missing or present) of two complex tones in a tone pair matched. Both tones were based on a five component harmonic series. Zero to three of the lowest components could be missing in the first tone, while the second (comparison) tone contained all five harmonics. The range of fundamentals tested varied from 200 to 600 Hz. Three monkeys learned to match tones missing their fundamentals to comparison harmonic complexes with the same pitch whereas the fourth monkey required the physical presence of the fundamental. Consideration of several cues available to the monkeys suggests that the animals could perceive the missing fundamental. The responses of 476 single units in auditory cortex of three alert rhesus monkeys to pure tones and harmonic complex tones were surveyed. Several neuron classes were ' identified, although responses varied over a continuum. "Filter" neurons had no inhibitory sidebands and responded well when any component of a complex tone entered its pure, tone receptive field. "Resolver" neurons had narrow tuning when,compared with filter neurons and had a frequency selectivity which was sufficient to resolve peaks within harmonic complex tone spectra. This frequency selectivity occurred over a limited dynamic range. "Fundamental" neurons exhibited similar tuning to pure tones and complex tones with their fundamentals. When a complex tone series without its fundamental was presented, these neurons did not respond, except when the physically present components entered its pure tone receptive field. This selectivity was caused by a powerful lower inhibitory sideband. "Wide band" neurons responded to complex tones and noise but had comparatively weak pure tone responses. "Narrow band" neurons responded selectively to pure tone stimuli, and poorly, if at all to complex tones and noise. The hypothetical function of arrays of these neurons was discussed. The responses of hypothetical neurons to pure and complex tones was .simulated using linear Gaussian filters to represent excitatory and inhibitory receptive fields. The effects on the neural response, of varying parameters of bandwidth, center frequency, and relative strength of inhibition versus excitation were tested. The behavior of each of the neuron types of the physiological experiment could be explained using various combinations of upper and lower inhibitory sidebands. It was found that the responses of filter neurons could be simulated using relatively broad excitatory receptive fields. Resolver neurons could be modelled with narrow (an octave or less in bandwidth) excitatory field, optionally flanked by narrow inhibitory sidebands, sharpening the filters. Fundamental neurons were simulated by the use of a powerful low frequency sideband. The response of narrow band neurons required powerful upper and lower inhibitory sidebands. Properties of large groups of neurons were simulated computationally to investigate the effectiveness of non-topographic representations in processing pitch and representing complex tones. Two neural networks, consisting of 640 units arranged in three layers, were trained to represent excitation patterns of harmonic complex tones with fundamental frequencies from 100 to 6500 Hz. The networks were trained using the generalized delta-rule for the back-propagation of error. It was demonstrated that tonotopy and bandlimited responses for individual units are not necessary for processing complex tones. One of the networks was trained to make associations necessary to perform a pitch matching task. When tested with excitation patterns of two tone complexes and missing fundamental complexes it was found that the most important feature of the input for determining the pattern of the output was the low frequency edge, or lowest component of the input pattern.

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