The representation of spectrally dynamic signals in primary auditory cortex

Abstract

Natural sounds, including speech, are traditionally characterized by their patterns of pitch, timbre, loudness, and modulation, all of which are closely related to the instantaneous spectrum of these signals. The central auditory system has developed elegant mechanisms to encode this spectro-temporal information. Thus, at the level of the primary auditory cortex (AI), the dynamic spectrum is repeatedly represented over a wide range of spectral and temporal resolutions. This multi-scale representation is derived from response patterns of cells selective to such parameters as local bandwidth, asymmetry of spectral peaks, and onset/offset transition rates. Physiological experiments measured the spectral and dynamic properties of neurons in AI using linear system analysis. One set of experiments used broadband signals with sinusoidally modulated spectral envelopes (‘‘ripples’’). Varying the density (or frequency), amplitude, phase, and velocity of the ripple pattern in a systematic fashion was used to derive a ripple transfer function. From this function it is possible to derive a spectro-temporal (impulse) response function via an inverse Fourier transform. A separate technique computed the neuronal impulse response (via reverse correlation) using signals with random and dynamic spectral characteristics. This presentation summarizes the application of these two analytical techniques to characterizing AI neuronal responses and discusses the scientific and technical implications of these multi-scale, cortical representations.