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SPONTANEOUS AND INFORMATION-TRIGGERED SEGMENTS OF SERIES OF HUMAN BRAIN ELECTRIC FIELD MAPS

Neural Information Processing Systems

SPONTANEOUS AND INFORMATION-TRIGGERED SEGMENTS OF SERIES OF HUMAN BRAIN ELECTRIC FIELD MAPS D. lehmann, D. Brandeis*, A. Horst, H. Ozaki* and I. Pal* Neurol09Y Department, University Hospital, 8091 Zurich, Switzerland ABSTRACT The brain works in a state-dependent manner: processin9 strate9ies and access to stored information depends on the momentary functional state which is continuously readjusted. The state is manifest as spatial confi9uration of the brain electric field. Spontaneous and information-tri9gered brain electric activity is a series of momentary field maps. Adaptive segmentation of spontaneous series into spatially stable epochs (states) exhibited 210 msec mean segments, discontinuous changes. Different maps imply different active neural populations, hence expectedly different effects on information processing: Reaction time differred between map classes at stimulus arrival.


A Computer Simulation of Olfactory Cortex with Functional Implications for Storage and Retrieval of Olfactory Information

Neural Information Processing Systems

A Computer Simulation of Olfactory Cortex With Functional Implications for Storage and Retrieval of Olfactory Information Matthew A. Wilson and James M. Bower Computation and Neural Systems Program Division of Biology, California Institute of Technology, Pasadena, CA 91125 ABSTRACT Based on anatomical and physiological data, we have developed a computer simulation of piriform (olfactory) cortex which is capable of reproducing spatial and temporal patterns of actual cortical activity under a variety of conditions. Using a simple Hebb-type learning rule in conjunction with the cortical dynamics which emerge from the anatomical and physiological organization of the model, the simulations are capable of establishing cortical representations for different input patterns. The basis of these representations lies in the interaction of sparsely distributed, highly divergent/convergent interconnections between modeled neurons. We have shown that different representations can be stored with minimal interference. Further, we have demonstrated that the degree of overlap of cortical representations for different stimuli can also be modulated. Both features are presumably important in classifying olfactory stimuli.


Mathematical Analysis of Learning Behavior of Neuronal Models

Neural Information Processing Systems

Please address all further correspondence to: John Y. Cheung School of EECS 202 W. Boyd, CEC 219 Norman, OK 73019 (405)325-4721 MATHEMATICAL ANALYSIS OF LEARNING BEHAVIOR OF NEURONAL MODELS John Y. Cheung and Massoud Omidvar School of Electrical Engineering and Computer Science ABSTRACT In this paper, we wish to analyze the convergence behavior of a number of neuronal plasticity models. Recent neurophysiological research suggests that the neuronal behavior is adaptive. In particular, memory stored within a neuron is associated with the synaptic weights which are varied or adjusted to achieve learning. A number of adaptive neuronal models have been proposed in the literature. Three specific models will be analyzed in this paper, specifically the Hebb model, the Sutton-Barto model, and the most recent trace model.


Distributed Neural Information Processing in the Vestibulo-Ocular System

Neural Information Processing Systems

DISTRIBUTED NEURAL INFORMATION PROCESSING IN THE VESTIBULO-OCULAR SYSTEM Clifford Lau Office of Naval Research Detach ment Pasadena, CA 91106 Vicente Honrubia* UCLA Division of Head and Neck Surgery Los Angeles, CA 90024 ABSTRACT A new distributed neural information-processing model is proposed to explain the response characteristics of the vestibulo-ocular system and to reflect more accurately the latest anatomical and neurophysiological data on the vestibular afferent fibers and vestibular nuclei. In this model, head motion is sensed topographically by hair cells in the semicircular canals. Hair cell signals are then processed by multiple synapses in the primary afferent neurons which exhibit a continuum of varying dynamics. The model is an application of the concept of "multilayered" neural networks to the description of findings in the bullfrog vestibular nerve, and allows us to formulate mathematically the behavior of an assembly of neurons whose physiological characteristics vary according to their anatomical properties. INTRODUCTION Traditionally the physiological properties of individual vestibular afferent neurons have been modeled as a linear time-invariant system based on Steinhausents description of cupular motion.


Bit-Serial Neural Networks

Neural Information Processing Systems

This arises from the parallelism and distributed knowledge representation which gives rise to gentle degradation as faults appear. These functions are attractive to implementation in VLSI and WSI. For example, the natural fault - tolerance could be useful in silicon wafers with imperfect yield, where the network degradation is approximately proportional to the non-functioning silicon area. To cast neural networks in engineering language, a neuron is a state machine that is either "on" or "off', which in general assumes intermediate states as it switches smoothly between these extrema. The synapses weighting the signals from a transmitting neuron such that it is more or less excitatory or inhibitory to the receiving neuron. The set of synaptic weights determines the stable states and represents the learned information in a system. The neural state, VI' is related to the total neural activity stimulated by inputs to the neuron through an activation junction, F. Neural activity is the level of excitation of the neuron and the activation is the way it reacts in a response to a change in activation.


Basins of Attraction for Electronic Neural Networks

Neural Information Processing Systems

In a useful associative memory, an initial state should lead reliably to the "closest" memory. This requirement suggests that a well-behaved basin of attraction should evenly surround its attractor and have a smooth and regular shape. One dimensional basin maps plotting "pull in" probability against Hamming distance from an attract or do not reveal the shape of the basin in the high dimensional space of initial states9.


SPONTANEOUS AND INFORMATION-TRIGGERED SEGMENTS OF SERIES OF HUMAN BRAIN ELECTRIC FIELD MAPS

Neural Information Processing Systems

SPONTANEOUS AND INFORMATION-TRIGGERED SEGMENTS OF SERIES OF HUMAN BRAIN ELECTRIC FIELD MAPS D. lehmann, D. Brandeis*, A. Horst, H. Ozaki* and I. Pal* Neurol09Y Department, University Hospital, 8091 Zurich, Switzerland ABSTRACT The brain works in a state-dependent manner: processin9 strate9ies and access to stored information depends on the momentary functional state which is continuously readjusted. The state is manifest as spatial confi9uration of the brain electric field. Spontaneous and information-tri9gered brain electric activity is a series of momentary field maps. Adaptive segmentation of spontaneous series into spatially stable epochs (states) exhibited 210 msec mean segments, discontinuous changes. Different maps imply different active neural populations, hence expectedly different effects on information processing: Reaction time differred between map classes at stimulus arrival.


An Optimization Network for Matrix Inversion

Neural Information Processing Systems

Box 150, Cheongryang, Seoul, Korea ABSTRACT Inverse matrix calculation can be considered as an optimization. We have demonstrated that this problem can be rapidly solved by highly interconnected simple neuron-like analog processors. A network for matrix inversion based on the concept of Hopfield's neural network was designed, and implemented with electronic hardware. With slight modifications, the network is readily applicable to solving a linear simultaneous equation efficiently. Notable features of this circuit are potential speed due to parallel processing, and robustness against variations of device parameters.


Schema for Motor Control Utilizing a Network Model of the Cerebellum

Neural Information Processing Systems

As a means of probing these cerebellar mechanisms, my colleagues and I have been conducting microelectrode studies of the neural messages that flow through the intermediate division of the cerebellum and onward to limb muscles via the rubrospinal tract. We regard this cerebellorubrospinal pathway as a useful model system for studying general problems of sensorimotor integration and adaptive brain function.


On the Power of Neural Networks for Solving Hard Problems

Neural Information Processing Systems

The neural network model is a discrete time system that can be represented by a weighted and undirected graph. There is a weight attached to each edge of the graph and a threshold value attached to each node (neuron) of the graph.