We have proposed a neural network style model of language processing in a na effort to build a cognitive model which would simultaneously satisfy constraints from psychology and neurophysiology. This model was successful in disambiguating word senses in semantically determined sentences, but was unable to distinguish Agent from Object in semantically reversible sentences such as "John loves Mary." In this paper we rectify the matter by specifying the syntactic portion of the model, which is a massively parallel, completely distributed connectionist parser. We also describe the results of a simulation of the model.

We describe a technique for automatically adapt- ing to the rate of an incoming signal. We first build a model of the signal using a recurrent net- work trained to predict the input at some delay, for a "typical" rate of the signal. Then, fixing the weights of this network, we adapt the time con- stant T of the network using gradient descent, adapting the delay appropriately as well. W e have found that on simple signals, the network adapts rapidly to new inputs varying in rate from twice as fast as the original signal, d o w n to ten times as slow. So far our results are based on linear rate changes. We discuss the possibilities of application to speech.