2001/03/01 by Didier A. Depireux, Jonathan Z. Simon, David J. Klein +1 · 3 citations
Neuroscience · Psychology · Mathematics · #Neural dynamics and brain function #Neuroscience and Music Perception #Hearing Loss and Rehabilitation #Transfer function #Representation (politics) #Separable space #Auditory cortex #Envelope (radar) #Function (biology) #Dynamics (music) #Acoustics #Communication #Computer science #Physics #Psychology #Mathematics #Neuroscience #Mathematical analysis #Telecommunications #Biology
paper · doi:10.1152/jn.2001.85.3.1220
openalex publication_date 2001/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/11
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, predicts, and fully characterizes the linear dynamics of neurons in response to sounds with rich spectro-temporal envelopes. It is computed from the responses to elementary "ripples," a family of sounds with drifting sinusoidal spectral envelopes. The collection of responses to all elementary ripples is the spectro-temporal transfer function. The complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples. 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 measure the responses to upward and downward drifting ripples, assuming reparability within each direction, to determine if the total bidirectional 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 but not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.