Auditory cortex on the exposed supratemporal plane in four anesthetized rhesus monkeys was mapped electrophysiologically with both pure-tone (PT) and broad-band complex sounds. The mapping confirmed the existence of at least three tonotopic areas. Primary auditory cortex, AI, was then aspirated, and the remainder of the cortex on the supratemporal plane was remapped. PT-responses in the caudomedial area, CM, were abolished in all animals but one, in which they were restricted to the high-frequency range. Some CM sites were still responsive to complex stimuli. In contrast to the effects on CM, no significant changes were detectable in the rostral area, R. After mapping cortex in four additional monkeys, injections were made with different tracers into matched best-frequency regions of AI, R, and CM. Injections in AI and R led to retrograde labeling of neurons in all three subdivisions of the medial geniculate (MG) nucleus (MGv, MGd, and MGm), as well as nuclei outside MG, whereas CM injections led to only sparse labeling of neurons in a restricted zone of the lateral MGd and, possibly, MGm, in addition to labeling in non-MG sites. The combined results suggest that MGv sends direct projections in parallel to areas AI and R, which drive PT-responses in both areas. PT-responses in area CM, however, appear to be driven by input relayed serially from AI. The direct input to CM from MGd and other thalamic nuclei may thus be capable of mediating responses only to broad-band sounds.

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Interest in the processing of optic flow has increased recently in both the neurophysiological and the psychophysical communities. We have designed a neural network model of the visual motion pathway in higher mammals that detects the direction of heading from optic flow. The model is a neural implementation of the subspace algorithm introduced by Heeger and Jepson (1990). We have tested the network in simulations that are closely related to psychophysical and neurophysiological experiments and show that our results are consistent with recent data from both fields. The network reproduces some key properties of human ego-motion perception. At the same time, it produces neurons that are selective for different components of ego-motion flow fields, such as expansions and rotations. These properties are reminiscent of a subclass of neurons in cortical area MSTd, the triple-component neurons. We propose that the output of such neurons could be used to generate a computational map of heading directions in or beyond MST.

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