To understand the neural representation of broadband, dynamic sounds in Primary Auditory Cortex (AI), we characterize responses using the Spectro-Temporal Response Field (STRF). The STRF describes and predicts the linear response of neurons to sounds with rich spectro-temporal envelopes. It is calculated here from the responses to elementary "ripples," a family of sounds with drifting, sinusoidal, spectral envelopes--the complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples.

The collection of responses to all elementary ripples is the spectro-temporal transfer function. Previous experiments using ripples with downward drifting spectra suggested that the transfer function is separable, i.e., it is reducible into a product of purely temporal and purely spectral functions.

Here we compare the responses to upward and downward drifting ripples, assuming separability within each direction, to determine if the total bi-directional transfer function is fully separable. In general, the combined transfer function for two directions is not symmetric, and hence units in AI are not, in general, fully separable. Consequently, many AI units have complex response properties such as sensitivity to direction of motion, though most inseparable units are not strongly directionally selective.

We show that for most neurons the lack of full separability stems from differences between the upward and downward spectral cross-sections, not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.