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The Journal of Neurophysiology Vol. 85 No. 3 March 2001, pp. 1220-1234
Copyright ©2001 by the American Physiological Society
1Institute for Systems Research and 2Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742-3311
Depireux, Didier A.,
Jonathan Z. Simon,
David J. Klein, and
Shihab A. Shamma.
Spectro-Temporal Response Field Characterization With Dynamic
Ripples in Ferret Primary Auditory Cortex. J. Neurophysiol. 85: 1220-1234, 2001. 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.
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