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A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions 1

A quantitative comparison of linear and non-linear models of...

引用

1st International IEEE EMBS Conference on Neural Engineering

作者： Gao, Yun Black, Michael J. Bienenstock, Elie Wu, Wei Donoghue, John P. Division of Applied Mathematics Brown University ProvidenceRI02912 United States Dept. of Computer Science Brown University Box 1910 ProvidenceRI02912 United States Dept. of Neuroscience Brown University ProvidenceRI02912 United States

ISBN: (纸本)0780375793

Many models have been proposed for the motor cortical encoding of arm motion. In particular, recent work has shown that simple linear models can be used to approximate the firing rates of a population of cells in primary motor cortex as a function of the position, velocity, and acceleration of the hand. Here we perform a systematic study of these linear models and of various non-linear generalizations. Specifically we consider linear Gaussian models, Generalized Linear Models (GLM), and Generalized Additive Models (GAM) of neural encoding. We evaluate their ability to represent the relationship between hand motion and neural activity, by looking at the likelihood of observed patterns of neural firing in a test data set and by evaluating the decoding performance of the different models (i.e. in terms of the error in reconstructing hand position from firing rates). To provide a level playing field for evaluating the decoding performance, we test all the models using a general recursive Bayesian estimator known as the particle filter, thus isolating the effect of the encoding model on reconstruction accuracy. © 2003 IEEE.

关键词： Encoding (symbols)

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Circuit optimization predicts dynamic networks for chemosensory orientation in the nematode Caenorhabditis elegans 03

Circuit optimization predicts dynamic networks for chemosens...

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Proceedings of the 17th International Conference on Neural Information Processing Systems

作者： Nathan A. Dunn John S. Conery Shawn R. Lockery Dept. of Computer Science University of Oregon Eugene OR Institute of Neuroscience University of Oregon Eugene OR

The connectivity of the nervous system of the nematode Caenorhabditis elegans has been described completely, but the analysis of the neuronal basis of behavior in this system is just beginning. Here, we used an optimization algorithm to search for patterns of connectivity sufficient to compute the sensorimotor transformation underlying C. elegans chemotaxis, a simple form of spatial orientation behavior in which turning probability is modulated by the rate of change of chemical concentration. Optimization produced differentiator networks with inhibitory feedback among all neurons. Further analysis showed that feedback regulates the latency between sensory input and behavior. Common patterns of connectivity between the model and biological networks suggest new functions for previously identified connections in the C. elegans nervous system.

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COOL (computer outreach opportunities for learning) project

COOL (computer outreach opportunities for learning) project

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2003 ASEE Annual Conference and Exposition: Staying in Tune with Engineering Education

作者： Collofello, James S. Urban, Joseph E. Anderson-Rowland, Mary R. Navabi, Faye Roman, Doris Arizona State University Dept. of Comp. Sci. and Engineering ASU Department of Computer Science CEAS Inclusive Lrng. Communities Program Arizona State University CEAS ASU Student Outreach Retention Program CEAS

The development of computer outreach opportunities for learning (COOL) project for secondary schools to provide computer programming education is discussed. The COOL project is a software development curriculum modules pilot program for secondary teachers to provide students with a better understanding of the software development field. The project also helps teachers in dispeling misconceptions and increasing the number and diversity of the students continuing their studies in preparation for software development careers. The COOL project has a freshman component fro incoming ASU students, consisting of placement test on computer skills expected for the introduction to engineering design course and the beginning course of Java.

关键词： Education

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Inserting safe memory reuse commands into ML-like programs 03

引用

Proceedings of the 10th international conference on Static analysis

作者： Oukseh Lee Hongseok Yang Kwangkeun Yi Dept. of Computer Science Research on Program Analysis System Korea Advanced Institute of Science and Technology

ISBN: (纸本)9783540403258

We present a static analysis that estimates reusable memory cells and a source-level transformation that adds explicit memory-reuse commands into the program text. For benchmark ML programs, our analysis and transformation achieves the memory reuse ratio from 5.2% to 91.3%. The small-ratio cases are for programs that have too prevalent sharings among memory cells. For other cases, our experimental results are encouraging in terms of accuracy and cost. Major features of our analysis are: (1) poly-variant analysis of functions by parameterization for the argument heap cells; (2) use of multiset formulas in expressing the sharings and partitionings of heap cells; (3) deallocations conditioned by dynamic flags that are passed as extra arguments to functions; (4) individual heap cell as the granularity of explicit memory-free. Our analysis and transformation is fully automatic.

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Neural decoding of cursor motion using a Kalman filter 15

Neural decoding of cursor motion using a Kalman filter

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16th Annual Neural Information Processing Systems Conference, NIPS 2002

作者： Wu, W. Black, M.J. Gao, Y. Bienenstock, E. Serruya, M. Shaikhouni, A. Donoghue, J.P. Division of Applied Mathematics Brown University Providence RI 02912 United States Dept. of Computer Science Brown University Providence RI 02912 United States Dept. of Neuroscience Brown University Providence RI 02912 United States Division of Biology and Medicine Brown University Providence RI 02912 United States

ISBN: (纸本)0262025507

The direct neural control of external devices such as computer displays or prosthetic limbs requires the accurate decoding of neural activity representing continuous movement. We develop a real-time control system using the spiking activity of approximately 40 neurons recorded with an electrode array implanted in the arm area of primary motor cortex. In contrast to previous work, we develop a control-theoretic approach that explicitly models the motion of the hand and the probabilistic relationship between this motion and the mean firing rates of the cells in 70ms bins. We focus on a realistic cursor control task in which the subject must move a cursor to "hit" randomly placed targets on a computer monitor. Encoding and decoding of the neural data is achieved with a Kalman filter which has a number of advantages over previous linear filtering techniques. In particular, the Kalman filter reconstructions of hand trajectories in off-line experiments are more accurate than previously reported results and the model provides insights into the nature of the neural coding of movement.

关键词： Kalman filters

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Humanoid Arm Motion Planning using Stereo Vision and RRT Search

Humanoid Arm Motion Planning using Stereo Vision and RRT Sea...

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2003 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Kagami, Satoshi Kuffner, James J. Nishiwaki, Koichi Okada, Kei Inaba, Masayuki Inoue, Hirochika Digital Human Lab. Natl. Inst. of Adv. Sci./Technology CREST Program Japan Sci./Technology Corporation School of Computer Science Carnegie Mellon University Dept. of Mechano-Informatics Sch. of Info. Science and Technology Univ. of Tokyo

This paper describes an experimental stereo vision based motion planning system for humanoid robots. The goal is to automatically generate arm trajectories that avoid obstacles in unknown environments from high-level task commands. Our system consists of three components: 1) environment sensing using stereo vision with disparity map generation and online consistency checking, 2) probabilistic mesh modeling in order to accumulate continuous vision input, and 3) motion planning for the robot arm using RRTs (Rapidly exploring Random Trees). We demonstrate results from experiments using an implementation designed for the humanoid robot H7.

关键词： Intelligent robots

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A CRCD experience: Integrating machine learning concepts into introductory engineering and science programming courses

A CRCD experience: Integrating machine learning concepts int...

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2003 ASEE Annual Conference and Exposition: Staying in Tune with Engineering Education

作者： Georgiopoulos, Michael Russell, Ingrid Castro, Jose Wu, Annie Kysilka, Marcella Demara, Ronald Gonzalez, Avelino Gelenbe, Erol Mollaghasemi, Mansooreh Sch. of Elec. Eng. and Comp. Science University of Central Florida Department of Computer science University of Hartford Intelligent Systems Focus Group IEEE-CS/ACM Computer Engineering Program University of Central Florid Education Foundations Department University of Central Florida Sch. of Elec. Eng. and Comp. Science Coll. of Eng. and Computer Science University of Central Florida Indust. Eng. and Mgmt. Sci. Dept. University of Central Florida

Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and science programming courses through appropriately designed programming projects.

关键词： Engineering education

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A switching Kalman filter model for the motor cortical coding of hand motion

A switching Kalman filter model for the motor cortical codin...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Wei Wu M.J. Black D. Mumford Yun Gao E. Bienenstock J.P. Donoghue Division of Applied Mathematics Brown University Providence RI Dept. of Computer Science Brown University Providence RI USA Division of Applied Mathematics Brown University Providence RI USA Dept. of Neuroscience Brown University Providence RI USA

We present a switching Kalman filter model (SKFM) for the real-time inference of hand kinematics from a population of motor cortical neurons. First we model the probability of the firing rates of the population at a particular time instant as a Gaussian mixture where the mean of each Gaussian is some linear function of the hand kinematics. This mixture contains a "hidden state", or weight, that assigns a probability to each linear, Gaussian, term in the mixture. We then model the evolution of this hidden state over time as a Markov chain. The expectation-maximization (EM) algorithm is used to fit this mixture model to training data that consists of measured hand kinematics (position, velocity, acceleration) and the firing rates of 42 units recorded with a chronically implanted multi-electrode array. Decoding of neural data from a separate test set is achieved using the switching Kaiman filter (SKF) algorithm. Quantitative results show that the SKFM outperforms the traditional linear Gaussian model in the decoding of hand movement. These results suggest that the SKFM provides a real-time decoding algorithm that may be appropriate for neural prosthesis applications.

关键词： Kinematics Decoding Neurons Training data Position measurement Velocity measurement Accelerometers Acceleration Testing Filters

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Mixed-excited phonetic vocoding at 265 bps

Mixed-excited phonetic vocoding at 265 bps

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： R. da S Maia R.J. da R Cirigliano D. Rojtenberg F.G.V. Resende Electrical Engineering Program/COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil Electrical Engineering Program/COPPE Dept. of Electronics and Computer Science/EPoli Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil

In this paper a phonetic vocoder which synthesizes speech using mixed excitation is presented. The encoder carries out HMM-based speech recognition and pitch analysis, whereas the decoder performs parameter extraction from HMM and builds a mixed excitation using pitch and bandpass voicing strengths. The vocoder at an average bit rate of 265 bit/s reaches good degree of intelligibility, while the use of mixed excitation significantly improves the speech quality with no increase of bit rate when compared with the conventional binary excitation pulse train/random noise.

关键词： Vocoders Speech synthesis Bit rate Speech recognition Speech analysis Performance analysis Decoding Parameter extraction Hidden Markov models Speech enhancement

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Characterization of neuroanatomic structures using 3D wavelet-based normal fields

Characterization of neuroanatomic structures using 3D wavele...

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International Symposium on Signal Processing and Its Applications (ISSPA)

作者： S.C.D. Pinto R.M. Cesar D. Gokcay Ld.F. Costa Cybemetic Vision Research Group IFSC Sao Paulo Brazil Cybernetic Vision Research Group - IFSC - USP São Carlos SP Brazil Dept. of Computer Science - IME - USP São Paulo SP Brazil Swartz Center for Computational Neuroscience San Diego CA USA

This paper presents a new approach for the morphometrical characterization of brain structures using shape features calculated from normal fields. An orientable surface representation of the brain structure is derived allowing the estimation of the corresponding normal field without the need of a parameterized surface. In fact, the normal field is calculated from the application of the 3D wavelet transform. Rotation- and translation-invariant shape features extracted from the normal fields are defined and successful experimental results using data from neuroanatomic structures are presented.

关键词： Shape Wavelet transforms computer science Brain Feature extraction Data mining computer vision Optical character recognition software Application software Tomography

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