Faculty Bibliography

Songbirds, which, like humans, learn complex vocalizations, provide an excellent model for the study of acoustic pattern recognition. Here we examined the role of three basic acoustic parameters in an ethologically relevant categorization task. Female zebra finches were first trained to classify songs as belonging to one of two males and then asked whether they could generalize this knowledge to songs systematically altered with respect to frequency, timing, or intensity. Birds' performance on song categorization fell off rapidly when songs were altered in frequency or intensity, but they generalized well to songs that were changed in duration by >25%. Birds were not deaf to timing changes, however; they detected these tempo alterations when asked to discriminate between the same song played back at two different speeds. In addition, when birds were retrained with songs at many intensities, they could correctly categorize songs over a wide range of volumes. Thus although they can detect all these cues, birds attend less to tempo than to frequency or intensity cues during song categorization. These results are unexpected for several reasons: zebra finches normally encounter a wide range of song volumes but most failed to generalize across volumes in this task; males produce only slight variations in tempo, but females generalized widely over changes in song duration; and all three acoustic parameters are critical for auditory neurons. Thus behavioral data place surprising constraints on the relationship between previous experience, behavioral task, neural responses, and perception. We discuss implications for models of auditory pattern recognition.

The organization of postthalamic auditory areas remains unclear in many respects. Using a stimulus based on properties of natural sounds, we mapped spectro-temporal receptive fields (STRFs) of neurons in the primary auditory area field L of unanesthetized zebra finches. Cells were sensitive to only a subset of possible acoustic features: nearly all neurons were narrowly tuned along the spectral dimension, the temporal dimension, or both; broadly tuned and strongly orientation-sensitive cells were rare. At high stimulus intensities, neurons were sensitive to differences in sound energy along their preferred dimension, while at lower intensities, neurons behaved more like simple detectors. Finally, we found a systematic relationship between neurons' STRFs, their electrophysiological properties, and their location in field L input or output layers. These data suggest that spectral and temporal processing are segregated within field L, and provide a unifying account of how field L response properties depend on stimulus intensity.

Neural activity in the frontal eye fields controls smooth pursuit eye movements, but the relationship between single neuron responses, cortical population responses, and eye movements is not well understood. We describe an approach to dynamically link trial-to-trial fluctuations in neural responses to parallel variations in pursuit and demonstrate that individual neurons predict eye velocity fluctuations at particular moments during the course of behavior, while the population of neurons collectively tiles the entire duration of the movement. The analysis also reveals the strength of correlations in the eye movement predictions derived from pairs of simultaneously recorded neurons and suggests a simple model of cortical processing. These findings constrain the primate cortical code for movement, suggesting that either a few neurons are sufficient to drive pursuit at any given time or that many neurons operate collectively at each moment with remarkably little variation added to motor command signals downstream from the cortex.

Songbird auditory neurons must encode the dynamics of natural sounds at many volumes. We investigated how neural coding depends on the distribution of stimulus intensities. Using reverse-correlation, we modeled responses to amplitude-modulated sounds as the output of a linear filter and a nonlinear gain function, then asked how filters and nonlinearities depend on the stimulus mean and variance. Filter shape depended strongly on mean amplitude (volume): at low mean, most neurons integrated sound over many milliseconds, while at high mean, neurons responded more to local changes in amplitude. Increasing the variance (contrast) of amplitude modulations had less effect on filter shape but decreased the gain of firing in most cells. Both filter and gain changes occurred rapidly after a change in statistics, suggesting that they represent nonlinearities in processing. These changes may permit neurons to signal effectively over a wider dynamic range and are reminiscent of findings in other sensory systems.