A switching between apparently coherent (oscillatory) and stochastic episodes of activity has been observed in responses from cat and monkey visual cortex. We describe the dynamics of these phenomena in two parallel approaches,a phenomenological and a rather microscopic one. On the one hand we analyze neuronal responses in terms of a hidden state model (HSM). The parameters of this model are extracted directly from experimental spiketrains. They characterize the underlying dynamics as well as the coupling of individual neurons to the network. This phenomenological modelthus provides a new framework for the experimental analysis of network dynamics.

We propose a model of the development of geometric reasoning in children that explicitly involves learning. The model uses a neural network that is initialized with an understanding of geometry similar to that of second-grade children. Through the presentation of a series of examples, the model is shown to develop an understanding of geometry similar to that of fifth-grade children who were trained using similar materials.

We describe two successfully working, analog VLSI vision circuits that move beyond pixel-based early vision algorithms. One circuit, implementing the dynamic wires model, provides for dedicated lines of communication among groups of pixels that share a common property. The chip uses the dynamic wires model to compute the arclength of visual contours. Another circuit labels all points inside a given contour with one voltage and all other with another voltage. Itsbehavior is very robust, since small breaks in contours are automatically sealed, providing for Figure-Ground segregation in a noisy environment. Both chips are implemented using networks of resistors and switches and represent a step towards object level processing since a single voltage value encodes the property of an ensemble of pixels.

The invariance of an objects' identity as it transformed over time provides a powerful cue for perceptual learning. We present an unsupervised learningprocedure which maximizes the mutual information between the representations adopted by a feed-forward network at consecutive time steps. We demonstrate that the network can learn, entirely unsupervised, to classify an ensemble of several patterns by observing pattern trajectories, even though there are abrupt transitions from one object to another between trajectories. Thesame learning procedure should be widely applicable to a variety of perceptual learning tasks. 1 INTRODUCTION A promising approach to understanding human perception is to try to model its developmental stages. There is ample evidence that much of perception is learned.

Basic connectionist principles imply that grammars should take the form of systems of parallel soft constraints defining an optimization problem the solutions to which are the well-formed structures in the language. Such Harmonic Grammars have been successfully applied to a number of problems in the theory of natural languages. Here it is shown that formal languages too can be specified by Harmonic Grammars, rather than by conventional serial rewrite rule systems. 1 HARMONIC GRAMMARS In collaboration with Geraldine Legendre, Yoshiro Miyata, and Alan Prince, I have been studying how symbolic computation in human cognition can arise naturally as a higher-level virtual machine realized in appropriately designed lower-level connectionist networks.The basic computational principles of the approach are these: (1) a. \Vhell analyzed at the lower level, mental representations are distributed patternsof connectionist activity; when analyzed at a higher level, these same representations constitute symbolic structures.

The planar thallium-201 myocardial perfusion scintigram is a widely used diagnostic technique for detecting and estimating the risk of coronary artery disease. Neural networks learned to interpret 100 thallium scintigrams asdetermined by individual expert ratings. Standard error backpropagation wascompared to standard LMS, and LMS combined with one layer of RBF units.

Likewise, the hardware realization of binary networks may prove simpler.

Simplified models of the lateral geniculate nucles (LGN) and striate cortexillustrate the possibility that feedback to the LG N may be used for robust, low-level pattern analysis. The information fed back to the LG N is rebroadcast to cortex using the LG N's full fan-out, so the cortex-LGN-cortex pathway mediates extensive cortico-cortical communication while keeping the number of necessary connectionssmall. 1 INTRODUCTION The lateral geniculate nucleus (LGN) in the thalamus is often considered as just a relay station on the way from the retina to visual cortex, since receptive field properties ofneurons in the LGN are very similar to retinal ganglion cell receptive field properties. However, there is a massive projection from cortex back to the LGN: it is estimated that 3-4 times more synapses in the LG N are due to corticogeniculate connectionsthan those due to retinogeniculate connections [12]. This suggests some important processing role for the LGN, but the nature of the computation performed has remained far from clear. I will first briefly summarize some anatomical facts and physiological results concerning thecorticogeniculate loop, and then present a simplified model in which its function is to (usefully) mediate communication between cortical cells.

Representations for semantic information about words are necessary formany applications of neural networks in natural language processing. This paper describes an efficient, corpus-based method for inducing distributed semantic representations for a large number ofwords (50,000) from lexical coccurrence statistics by means of a large-scale linear regression. The representations are successfully appliedto word sense disambiguation using a nearest neighbor method. 1 Introduction Many tasks in natural language processing require access to semantic information about lexical items and text segments.

Fourier transform, is widely used in analyzing non-stationary signals, such speech.