Two tightly coupled subproblems need to be solved for locating and recognizing the model: the correspondence problem (how are scene features put into correspondence with model features?),

The conventional Bayesian justification of backprop is that it finds the MAP weight vector. As this paper shows, to find the MAP io function instead one must add a correction tenn to backprop. That tenn biases one towards io functions with small description lengths, and in particular favors (some kinds of) feature-selection, pruning, and weight-sharing.

We consider the problem of how the CNS learns to control dynamics of a mechanical system. By using a paradigm where a subject's hand interacts with a virtual mechanical environment, we show that learning control is via composition of a model of the imposed dynamics. Some properties of the computational elements with which the CNS composes this model are inferred through the generalization capabilities of the subject outside the training data.

Biological neurons have a variety of intrinsic properties because of the large number of voltage dependent currents that control their activity. Neuromodulatory substances modify both the balance of conductances that determine intrinsic properties and the strength of synapses. These mechanisms alter circuit dynamics, and suggest that functional circuits exist only in the modulatory environment in which they operate. 1 INTRODUCTION Many studies of artificial neural networks employ model neurons and synapses that are considerably simpler than their biological counterparts.

The distance measure and learning problem are formally described as nested objective functions. We derive an efficient algorithm by using optimization techniques that allow us to divide up the objective function into parts which may be minimized in distinct phases. The algorithm has accurately recreated 10 prototypes from a randomly generated sample database of 100 images consisting of 20 points each in 120 experiments. Finally, by incorporating permutation invariance in our distance measure, we have a technique that we may be able to apply to the clustering of graphs. Our goal is to develop measures which will enable the learning of objects with shape or structure. Acknowledgements This work has been supported by AFOSR grant F49620-92-J-0465 and ONR/DARPA grant N00014-92-J-4048.

Although there are many ways to measure efficiency, we shall be concerned with node complexity, which as its name implies, is a calculation of the required number of nodes. Node complexity is a useful measure of efficiency since the amount of resources required to implement or even simulate a recurrent neural network is typically related to the number of nodes. Node complexity can also be related to the efficiency of learning algorithms for these networks and perhaps to their generalization ability as well. We shall focus on the node complexity of recurrent neural network implementations of finite state machines (FSMs) when the nodes of the network are restricted to threshold logic units.

Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism of these functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency within the realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature on functional models of selective attention and context dependency.

What is the'correct' theoretical description of neuronal activity? The analysis of the dynamics of a globally connected network of spiking neurons (the Spike Response Model) shows that a description by mean firing rates is possible only if active neurons fire incoherently. If firing occurs coherently or with spatiotemporal correlations, the spike structure of the neural code becomes relevant. Alternatively, neurons can be gathered into local or distributed ensembles or'assemblies'. A description based on the mean ensemble activity is, in principle, possible but the interaction between different assemblies becomes highly nonlinear. A description with spikes should therefore be preferred.

E represents the expectation on random number K of hidden units (1:::; I\:::; n). This relationship makes it possible to characterize explicitly how a regularization term affects bias/variance of networks.

The general goal of this workshop was to bring t.ogether researchers working toward developing a theoretical framework for the analysis and design of neural networks. The t.echnical focus of the workshop was to address recent. The primary topics addressed the following three areas: 1) Computational complexity issues in neural networks, 2) Complexity issues in learning, and 3) Convergence and numerical properties of learning algorit.hms. Such st.udies, in t.urn, have generated considerable research interest. A similar development can be observed in t.he area of learning as well: Techniques primarily developed in the classical theory of learning are being applied to understand t.he generalization and learning characteristics of neural networks.