Magnetoencephalography recordings from the auditory cortex of subjects listening to synthetic vowels show a close correlation between the timing of the evoked M100 response and the first formant frequency (F1). These results are consistent with evoked magnetic field latencies elicited by tone stimuli, which show 100 to 300-Hz tones associated with latencies up to 30 ms longer than 500 to 3000-Hz tones. In experiment 1, three-formant vowels /u,a,i/ were presented at two fundamental frequencies (F0=100 Hz, 200 Hz). The M100 latency was a function of the vowel identity and not F0: M100 was significantly shorter for /a/ than /u/, consistent with the spectral center of gravity being at a higher frequency for /a/. In experiment 2, single-formant vowels (/u/:F1=330 Hz, /a/:F1=720 Hz) were covaried with two F0 values. M100 latencies were shorter for /a/ (high F1) than for /u/ (low F1), at both fundamentals. In experiment 3, subjects listened to pure-tone complexes with frequencies and amplitudes matching the F0 and F1 energy peaks of the stimuli in experiment 2. M100 latencies showed the same pattern: latency covaried with the energy peak corresponding to F1, suggesting that the sensitivity to the energy in the F1 range is not speech-specific. Finally, an experiment involving two-formant, amplitude-varying vowels will be presented.
Control system design for vehicles with highly nonlinear, tinievarying, or poorly-modeled dynamics poses serious difficulties for all currently advocated design methodologies. These difficulties arise in the design of current aerospace and underwater vehicles and their solution will be crucial for proposed autonomous vehicles. In the present paper we propose the use of connectionist systems as learning controllers. The ability of connectionist systems to approximate arbitrary continuous functions (e.g., control laws) overcomes the usual memory intensive nature of learning systems. We extend the backpropagation algorithm to allow the connectionist system to learn to function as a closed-loop controller and to force the dynamics of the closed-loop system to match the prespecified dynamics of a reference system. An example of the application of this algorithm to the depth control of an autonomous underwater vehicle is included. IntroductionThe control system design for vehicles with highly nonlinear, time-varying, or poorly-modeled dynamics poses serious difficulties for all currently advocated design methodologies. These difficulties arise in the context of designing control systems for a broad spectrum of aerospace and underwater vehicles. In these instances the problem is compocnded by an inability to accurately predict or measure many of the parameters needed to construct a "good" dynamic model of the vehicle, and by the nonlinear dynamics encountered throughout the operational envelope of the vehicle.In this paper we investigate the use of connectionist systems as learning controllers. A connectionist system is a set of interconnected simple processors. One specific example of :i connectionist system, the neural network, was originally studied as simple model of the interconnected neurons in a nervous system. For this application we are not so much interested in connectionist systems for their biological implications as for the fact that feedforward connectionist systems with at least one hidden layer have been shown to be dense (under
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