The ensemble dynamics of stochastic learning algorithms can be studied using theoretical techniques from statistical physics. We develop the equations of motion for the weight space probability densities for stochastic learning algorithms. We discuss equilibria in the diffusion approximation and provide expressions for special cases of the LMS algorithm. The equilibrium densities are not in general thermal (Gibbs) distributions in the objective function being minimized,but rather depend upon an effective potential that includes diffusion effects. Finally we present an exact analytical expression for the time evolution of the density for a learning algorithm withweight updates proportional to the sign of the gradient.

This paper describes an approach to integrated segmentation and recognition of hand-printed characters. The approach, called Saccade, integrates ballistic and corrective saccades (eye movements) with character recognition. A single backpropagation net is trained to make a classification decision on a character centered in its input window, as well as to estimate the distance of the current and next character from the center of the input window. The net learns to accurately estimate these distances regardless of variations in character width, spacing between characters, writing style and other factors.

We have designed a neural network which detects the direction of egomotion fromoptic flow in the presence of eye movements (Lappe and Rauschecker, 1993). The performance of the network is consistent with human psychophysical data, and its output neurons show great similarity to "triple component" cells in area MSTd of monkey visual cortex. We now show that by using assumptions about the kind of eye movements that the obsenrer is likely to perform, our model can generate various other cell types found in MSTd as well.

In this work we apply a texture classification network to remote sensing image analysis.The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain. 1 INTRODUCTION In this work we apply a texture classification network to remote sensing image analysis. The goal is to segment the input image into homogeneous textured regions and identify each region as one of a prelearned library of textures, e.g.

For example, a moving stimulus appears smeared when viewed briefly (30 ms), yet sharp when viewed for a longer exposure (100 ms) (Burr, 1980).

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.

We are interested in the use of analog neural networks for recognizing visualobjects. Objects are described by the set of parts they are composed of and their structural relationship. Structural modelsare stored in a database and the recognition problem reduces to matching data to models in a structurally consistent way.The object recognition problem is in general very difficult in that it involves coupled problems of grouping, segmentation and matching. We limit the problem here to the simultaneous labelling ofthe parts of a single object and the determination of analog parameters. This coupled problem reduces to a weighted match problem in which an optimizing neural network must minimize E(M,p) LO'i MO'i WO'i(p), where the {MO'd are binary match variables for data parts i to model parts a and {Wai(P)} are weights dependent on parameters p .

In visual processing the ability to deal with missing and noisy information iscrucial. Occlusions and unreliable feature detectors often lead to situations where little or no direct information about features is available. Howeverthe available information is usually sufficient to highly constrain the outputs. We discuss Bayesian techniques for extracting class probabilities given partial data. The optimal solution involves integrating overthe missing dimensions weighted by the local probability densities. We show how to obtain closed-form approximations to the Bayesian solution using Gaussian basis function networks.

The classical computational model for stereo vision incorporates a uniqueness inhibition constraint to enforce a one-to-one feature match, thereby sacrificing the ability to handle transparency. Critics ofthe model disregard the uniqueness constraint and argue that the smoothness constraint can provide the excitation support required for transparency computation.

We present a model of how MT cells aggregate responses from VI to form such a velocity representation. Two different sets of units, with local receptive fields, receive inputs from motion energy filters. One set of units forms estimates of local motion, while the second set computes the utility of these estimates. Outputs from this second set of units "gate" the outputs from the first set through a gain control mechanism. This active process for selecting only a subset of local motion responses to integrate into more global responses distinguishes our model from previous models of velocity estimation.