An approach is presented to learning high dimensional functions in the case where the learning algorithm can affect the generation of new data. A local modeling algorithm, locally weighted regression, is used to represent the learned function. Architectural parameters of the approach, such as distance metrics, are also localized and become a function of the query point instead of being global. Statistical tests are given for when a local model is good enough and sampling should be moved to a new area. Our methods explicitly deal with the case where prediction accuracy requirements exist during exploration: By gradually shifting a "center of exploration" and controlling the speed of the shift with local prediction accuracy, a goal-directed exploration of state space takes place along the fringes of the current data support until the task goal is achieved.

The most commonly used neural network models are not well suited to direct digital implementations because each node needs to perform a large number of operations between floating point values. Fortunately, the ability to learn from examples and to generalize is not restricted to networks ofthis type. Indeed, networks where each node implements a simple Boolean function (Boolean networks) can be designed in such a way as to exhibit similar properties. Two algorithms that generate Boolean networks from examples are presented. The results show that these algorithms generalize very well in a class of problems that accept compact Boolean network descriptions. The techniques described are general and can be applied to tasks that are not known to have that characteristic. Two examples of applications are presented: image reconstruction and handwritten character recognition.

Many well known neural network techniques for adaptive pattern classification and function approximation are inherently highly parallel, and thus have proven difficult to implement for real-time applications at a reasonable cost.

Dimension reduction provides compact representations for storage, transmission, and classification. Dimension reduction algorithms operate by identifying and eliminating statistical redundancies in the data. The optimal linear technique for dimension reduction is principal component analysis (PCA).

Maps of orientation preference and ocular dominance were recorded optically from the cortices of 5 infant macaque monkeys, ranging in age from 3.5 to 14 weeks. In agreement with previous observations, we found that basic features of orientation and ocular dominance maps, as well as correlations between them, are present and robust by 3.5 weeks of age. We did observe changes in the strength of ocular dominance signals, as well as in the spacing of ocular dominance bands,both of which increased steadily between 3.5 and 14 weeks of age. The latter finding suggests that the adult spacing of ocular dominance bands depends on cortical growth in neonatal animals. Since we found no corresponding increase in the spacing of orientation preferences, however, there is a possibility that the orientation preferences of some cells change as the cortical surface expands. Since correlations between the patterns of orientation selectivity and ocular dominance are present at an age, when the visual system is still immature, it seems more likely that their development maybe an innate process and may not require extensive visual experience.

We propose a computational model for how the cortex discriminates shape and depth from texture. The model consists of four stages: (1) extraction of local spatial frequency, (2) frequency characterization, (3) detection of texture compression by normalization, and (4) integration of the normalized frequency over space. The model accounts for a number of psychophysical observations including experiments based on novel random textures. These textures are generated from white noise and manipulated in Fourier domain in order to produce specific frequency spectra. Simulations with a range of stimuli, including real images, show qualitative and quantitative agreement with human perception. 1 INTRODUCTION There are several physical cues to shape and depth which arise from changes in projection as a surface curves away from view, or recedes in perspective.

We will present the implementation of intelligent electronic circuits realized for the first time using a new functional device called Neuron MOS Transistor (neuMOS or vMOS in short) simulating the behavior of biological neurons at a single transistor level. Search for the most resembling data in the memory cell array, for instance, can be automatically carried out on hardware without any software manipulation. Soft Hardware, which we named, can arbitrarily change its logic function in real time by external control signals without any hardware modification. Implementation of a neural network equipped with an on-chip self-learning capability is also described. Through the studies of vMOS intelligent circuit implementation, we noticed an interesting similarity in the architectures of vMOS logic circuitry and biological systems.

Eaton E. P. O. Biology University of Colorado Boulder, Co. 80309 Abstract We provide a computational description of the function of the Mauthner system.This is the brainstem circuit which initiates faststart escapes in teleost fish in response to sounds. Our simulations, usingbackpropagation in a realistically constrained feedforward network, have generated hypotheses which are directly interpretable interms of the activity of the auditory nerve fibers, the principle cells of the system and their associated inhibitory neurons. 1 INTRODUCTION 1.1 THE M.AUTHNER SYSTEM Much is known about the brainstem system that controls fast-start escapes in teleost fish. The most prominent feature of this network is the pair of large Mauthner cells whose axons cross the midline and descend down the spinal cord to synapse on primary motoneurons. The Mauthner system also includes inhibitory neurons, the PHP cells, which have a unique and intense field effect inhibition at the spikeinitiating zoneof the Mauthner cells (Faber and Korn, 1978). The Mauthner system is part of the full brainstem escape network which also includes two pairs of cells homologous to the Mauthner cell and other populations of reticulospinal neurons. With this network fish initiate escapes only from appropriate stimuli, turn away from the offending stimulus, and do so very rapidly with a latency around 15 msec in goldfish.

Sejnowski Computational Neurobiology Lab The Salk: Institute San Diego, CA 92138 Visual spatial information is projected from the retina to the brain in a highly topographic fashion, so that 2-D visual space is represented in a simple retinotopic map. Auditory spatial information, by contrast, has to be computed from binaural time and intensity differences as well as from monaural spectral cues produced by the head and ears. Evaluation of these cues in the central nervous system leads to the generation of neurons that are sensitive to the location of a sound source in space ("spatial tuning") and, in some animal species, to auditory space maps where spatial location is encoded as a 2-D map just like in the visual system. The brain structures thought to be involved in the multimodal integration of visual and auditory spatial integration are the superior colliculus in the midbrain and the inferior parietal lobe in the cerebral cortex. It has been suggested for the owl that the visual system participates in setting up the auditory space map in the superior.

University of Rochester) and ICSIM (lCSI Berkeley) allow the definition of unit types and complex connectivity patterns. On a very high level of abstraction, simulators like tleam (UCSD) allow the easy realization of predefined network architectures (feedforwardnetworks) and leaming algorithms such as backpropagation. Ben Gomes, International Computer Science Institute (Berkeley) introduced the Connectionist Supercomputer 1. The CNSl is a multiprocessor system designed for moderate precision fixed point operations used extensively in connectionist network calculations. Custom VLSI digital processors employ an on-chip vector coprocessor unit tailored for neural network calculations and controlled by RISC scalar CPU. One processor and associated commercial DRAM comprise a node, which is connected in a mesh topology with other nodes to establish a MIMD array. One edge of the communications meshis reserved for attaching various 110 devices, which connect via a custom network adaptor chip. The CNSl operates as a compute server and one 110 port is used for connecting to a host workstation. Users with mainstream connectionist applications can use CNSim, an object-oriented, graphical high-level interface to the CNSl environment.