Sensitivity of neurons in cat primary auditory cortex to tones and frequency-modulated stimuli. II: Organization of response properties along the 'isofrequency' dimension

The spatial distribution of neuronal responses to tones and frequency-modulated (FM) stimuli was mapped along the 'isofrequency' dimension of the primary auditory cortex (AI) of barbiturate-anesthetized cats. In each cat, electrode penetrations roughly orthogonal to the cortical surface were closely spaced (average separation ≈ 130 μm) along the dorsoventral extent of a single 'isofrequency' strip in high frequency parts of AI (> 15 kHz). Characteristic frequency (CF), minimum threshold, sharpness of frequency tuning (Q₁₀ and Q₂₀), the dynamic range of the spike count-intensity function at CF, sensitivity to the rate of change of frequency (RCF) and to the direction of frequency-modulation (DS) were determined for contralaterally-presented tone and FM stimuli. Sharpness of tuning attained maximum values at central loci along the dorsoventral 'isofrequency' axis and values declined towards more dorsal and more ventral locations. Minimum threshold and dynamic range varied between high and low values in a similar and correlated periodic fashion. Their combined organization yielded an orderly spatial representation of response strength, relative to maximum, as a function of stimulus amplitude. The distributions of the most common forms of FM rate sensitivity (RCF response categories) and best RCF along 'isofrequency' strips were significantly non-random although there was a considerable degree of variability between cats. FM directional preference and sensitivity appeared to be randomly distributed. Sharpness of tuning may be related to the analysis of the spectral content of an acoustic stimulus, both minimum threshold and dynamic range are related to the encoding of stimulus intensity, and measures of FM rate and directional sensitivity assess the coding of temporal changes of stimulus spectra. The independent, or for minimum threshold and dynamic range dependent, topographic organizations of these neuronal parameters therefore suggest parallel and independent processing of these aspects of acoustic signals in AI.