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作者： D.R. Edgington K.A. Salamy M. Risi R.E. Sherlock D. Walther Christof Koch Monterey Bay Aquarium Research Institute Moss Landing CA USA Computation and Neural Systems Program California Institute of Technology Pasadena CA USA

We present an attentional selection system for processing video streams from remotely operated underwater vehicles (ROVs). The system identifies potentially interesting visual events spanning multiple frames based on low-level spatial properties of salient tokens, which are associated with those events and tracked over time. If video frames contain interesting events, they are labeled "interesting", otherwise they are labeled "boring". By marking the interesting events and omitting "boring" frames in the output stream, we augment the productivity of human video annotators, or, alternatively, provide input for a subsequent object classification algorithm.

关键词： Event detection Remotely operated vehicles Animals Cameras Layout Streaming media Underwater tracking Productivity Humans Object detection

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Adaptation of current signals with floating-gate circuits

Adaptation of current signals with floating-gate circuits

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作者： Pesavento, Alberto Horiuchi, Timothy Diorio, Chris Koch, Christof Department of Electrical Engineering California Institute of Technology Pasadena CA 91125 United States Department of Electrical and Computer Engineering University of Maryland College Park MD 20742 United States Department of Computer Science and Engineering University of Washington Seattle WA 98195-2350 United States Computation and Neural Systems Program California Institute of Technology Pasadena CA 91125 United States

In this paper we present a new, adaptive spatial-derivative circuit for CMOS image sensors. The circuit removes its offset as a natural part of its operation using a combination of electron tunneling and hot-electron injection to add or remove charge on a floating-gate of an auto-zeroing amplifier. We designed, fabricated and successfully tested a chip with the circuit. Test results show that the circuit reduces the offsets by more than an order of magnitude.

关键词： Image sensors

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Generalized smoothing networks in early vision

Generalized smoothing networks in early vision

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： S.-C. Liu J.G. Harris Rockwell Science Center Inc. Thousand Oaks CA USA California Institute of Technology 216-76 Computation and Neural Systems Program Pasadena CA USA

Generalized smoothing networks have been developed which enforce smoothness constraints for any arbitrary level of derivative of the input data. Furthermore, discontinuities of any order of derivative can be detected by providing for continuous line processes, which selectively inhibit smoothing. Second- and higher-order networks are required for many problems in early vision; first-order networks are often unsatisfactory. Examples in surface interpolation, edge detection, and image segmentation are shown. Solution of these types of problems typically takes a prohibitive amount of time, even on supercomputers. A significant advantage of these proposed networks is that they can be mapped directly to analog VLSI hardware.< >

关键词： Smoothing methods Intelligent networks Image edge detection Image segmentation Very large scale integration Interpolation Voltage Supercomputers neural network hardware Computer vision

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Fast learning with predictive forward models 91

Fast learning with predictive forward models

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Proceedings of the 5th International Conference on neural Information Processing systems

作者： Carlos Brody Computation and Neural Systems Program California Institute of Technology Pasadena CA and Dept. of Computer Science IIMAS UNAM México D.F. México

ISBN: (纸本)9781558602229

A method for transforming performance evaluation signals distal both in space and time into proximal signals usable by supervised learning algorithms, presented in [Jordan & Jacobs 90], is examined. A simple observation concerning differentiation through models trained with redundant inputs (as one of their networks is) explains a weakness in the original architecture and suggests a modification: an internal world model that encodes action-space exploration and, crucially, cancels input redundancy to the forward model is added. Learning time on an example task, cartpole balancing, is thereby reduced about 50 to 100 times.

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Feedforward architectures driven by inhibitory interactions

arXiv

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arXiv 2017年

作者： Billeh, Yazan N. Schaub, Michael T. Computation and Neural Systems Program California Institute of Technology Université Catholique de Louvain Massachusetts Institute of Technology Institute for Data Systems and Society Massachusetts Institute of Technology Department of Engineering Science University of Oxford

Directed information transmission is paramount for many social, physical, and biological sys- tems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neu- rons play only a stabilizing role for the network dy- namics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the di- versity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random. This is achieved by endowing inhibitory nodes with a more active role in the network. Our findings demonstrate that apparent feedforward activity can be caused by a much broader network- architectural basis than often assumed. Copyright © 2017, The Authors. All rights reserved.

关键词： Brain

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Computing optical flow in resistive networks and in the primate visual system

Computing optical flow in resistive networks and in the prim...

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Workshop on Visual Motion

作者： C. Koch H.T. Wang B. Mathur A. Hsu H. Suarez Computation and Neural Systems Program Divisions of Biology and Engineering and Applied Sciences California Institute of Technology Pasadena CA USA Science Center Rockwell International Thousand Oaks CA USA

It is shown how the well-known algorithm of B. Horn and B.C. Schunk (1981) for computing optical flow, based on minimizing a quadratic functional using a relaxation scheme, maps onto two different kinds of massive parallel hardware: either resistive networks which are attractive for their technological potential, or neuronal networks related to the ones occurring in the motion pathway in the primate's visual system. If the x and y components of the motion field are coded explicitly as voltages within electrical circuits, simple resistive networks solve for the optical flow in the presence of motion discontinuities. These networks are being implemented into analog, subthreshold CMOS VLSI (complementary metal oxide semiconductor very large-scale integration) circuits. If velocity is represented within a population of direction selective cells, the resulting neuronal network maps onto the primate's striate and extrastriate visual cortex (middle temporal area). The performance of the network mimicks a large number of psychological illusions as well as electrophysical findings.< >

关键词： Optical computing Computer networks Optical fiber networks Image motion analysis Biological neural networks Circuits Concurrent computing Hardware Visual system Voltage

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Incorporating contextual information in white blood cell identification 97

Incorporating contextual information in white blood cell ide...

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Proceedings of the 11th International Conference on neural Information Processing systems

作者： Xubo Song Yaser Abu-Mostafa Joseph Sill Department of Electrical Engineering California Institute of Technology Pasadena CA Dept. of Electrical Engineering and Dept. of Computer Science California Institute of Technology Pasadena CA Computation and Neural Systems Program California Institute of Technology Pasadena CA

In this paper we propose a technique to incorporate contextual information into object classification. In the real world there are cases where the identity of an object is ambiguous due to the noise in the measurements based on which the classification should be made. It is helpful to reduce the ambiguity by utilizing extra information referred to as context, which in our case is the identities of the accompanying objects. This technique is applied to white blood cell classification. Comparisons are made against "no context" approach, which demonstrates the superior classification performance achieved by using context. In our particular application, it significantly reduces false alarm rate and thus greatly reduces the cost due to expensive clinical tests.

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Modeling the modulatory effect of attention on human spatial vision 01

Modeling the modulatory effect of attention on human spatial...

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Proceedings of the 14th International Conference on neural Information Processing systems: Natural and Synthetic

作者： Laurent Itti Jochen Braun Christof Koch Computer Science Department University of Southern California Los Angeles CA Institute of Neuroscience and School of Computing University of Plymouth Plymouth Devon U.K. Computation and Neural Systems Program California Institute of Technology Pasadena CA

We present new simulation results, in which a computational model of interacting visual neurons simultaneously predicts the modulation of spatial vision thresholds by focal visual attention, for five dual-task human psychophysics experiments. This new study complements our previous findings that attention activates a winner-take-all competition among early visual neurons within one cortical hypercolumn. This "intensified competition" hypothesis assumed that attention equally affects all neurons, and yielded two single-unit predictions: an increase in gain and a sharpening of tuning with attention. While both effects have been separately observed in electrophysiology, no single-unit study has yet shown them simultaneously. Hence, we here explore whether our model could still predict our data if attention might only modulate neuronal gain, but do so non-uniformly across neurons and tasks. Specifically, we investigate whether modulating the gain of only the neurons that are loudest, best-tuned, or most informative about the stimulus, or of all neurons equally but in a task-dependent manner, may account for the data. We find that none of these hypotheses yields predictions as plausible as the intensified competition hypothesis, hence providing additional support for our original findings.

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An adaptive multi-scale approach for estimating optical flow: computational theory and physiological implementation

An adaptive multi-scale approach for estimating optical flow...

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Workshop on Visual Motion

作者： C. Koch H.T. Wang R. Battiti B. Mathur C. Ziomkowski Computation and Neural Systems Program California Institute of Technology Pasadena CA USA Science Center Rockwell International Thousand Oaks CA USA Dipartimento di Fisica Universita di Genova Genoa Italy

The accuracy of optical flow estimation depends on the spatio-temporal discretization used in the computation. The authors propose an adaptive multiscale method, where the discretization scale is chosen locally according to an estimate of the relative error in the velocity measurements. They show that their coarse-to-fine method provides substantially better results of optical flow than conventional algorithms. The authors map this multiscale strategy onto their model of motion computation in primate area MT. The model consists of two stages: (1) local velocities are measured across multiple spatio-temporal channels, while (2) the optical flow field is computed by a network of direction-selective neurons at multiple spatial resolutions. Their model neurons show the same nonclassical receptive field properties as Allman's type I MT neurons and lead to a novel interpretation of some aspect of the motion capture illusion.< >

关键词： Adaptive optics Image motion analysis Optical computing Neurons Velocity measurement Computer networks computational modeling Area measurement Fluid flow measurement Motion measurement

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Viewpoints: Approaches to defining and investigating fear

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NATURE NEUROSCIENCE 2019年第8期22卷 1205-1216页

作者： Mobbs, Dean Adolphs, Ralph Fanselow, Michael S. Barrett, Lisa Feldman LeDoux, Joseph E. Ressler, Kerry Tye, Kay M. Department of Humanities and Social Sciences and Computation and Neural Systems Program California Institute of Technology Pasadena California USA Departments of Psychology and Psychiatry and Biobehavioral Sciences University of California Los Angeles Los Angeles California USA Department of Psychology Northeastern University Boston Massachusetts USA Martinos Center for Biomedical Imaging and Department of Psychiatry Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA Center for Neural Science New York University New York New York USA Nathan Kline Institute New York State Office of Mental Health New York New York USA Departments of Psychiatry and Child and Adolescent Psychiatry NYU Langone Medical School New York New York USA Division of Depression & Anxiety Disorders McLean Hospital Belmont Massachusetts USA Department of Psychiatry at Harvard Medical School Boston Massachusetts USA Salk Institute for Biological Studies La Jolla California USA

There is disagreement on how best to define and investigate fear. Nature Neuroscience asked Dean Mobbs to lead experts from the fields of human and animal affective neuroscience to discuss their viewpoints on how to d... 详细信息

关键词： Amygdala Psychology

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