检索结果-内蒙古大学图书馆

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

作者： Hyuk Oh Rodolphe J. Gentili James A. Reggia Jose L. Contreras-Vidal Neuroscience and Cognitive Science Program University of Maryland Department of Kinesiology and the Neuroscience and Cognitive Science Program University of Maryland Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies University of Maryland Department of Kinesiology the Neuroscience and Cognitive Science Program and the Fischell Department of Bioengineering University of Maryland

ISBN: (纸本)9781424441211

It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator;ii) the inverse model of the imitator's frontal mirror neuron system can be trained to provide the motor plans for the imitated actions.

关键词： Mirror Neurons imitation Premotor Cortex dimensional relationship Superior parietal gyrus Superior Temporal Sulcus Intraparietal Sulcus arithmetic

来源：评论

学校读者我要写书评

暂无评论

Three theorems: Brain-like networks logically reason and optimally generalize

Three theorems: Brain-like networks logically reason and opt...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Juyang Weng Department of Computer Science and Engineering Cognitive Science Program and Neuroscience Program Michigan State University East Lansing MI USA

Finite Automata (FA) is a base net for many sophisticated probability-based systems of artificial intelligence. However, an FA processes symbols, instead of images that the brain senses and produces (e.g., sensory images and motor images). Of course, many recurrent artificial neural networks process images. However, their non-calibrated internal states prevent generalization, let alone the feasibility of immediate and error-free learning. I wish to report a general-purpose Developmental program (DP) for a new type of, brain-anatomy inspired, networks - Developmental Networks (DNs). The new theoretical results here are summarized by three theorems. (1) From any complex FA that demonstrates human knowledge through its sequence of the symbolic inputs-outputs, the DP incrementally develops a corresponding DN through the image codes of the symbolic inputs-outputs of the FA. The DN learning from the FA is incremental, immediate and error-free. (2) After learning the FA, if the DN freezes its learning but runs, it generalizes optimally for infinitely many image inputs and actions based on the embedded inner-product distance, state equivalence, and the principle of maximum likelihood. (3) After learning the FA, if the DN continues to learn and run, it “thinks” optimally in the sense of maximum likelihood based on its past experience.

关键词： Neurons Tin Automata Humans Brain models Learning systems

来源：评论

学校读者我要写书评

暂无评论

A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

引用

Advances in Artificial Neural Systems 2012年第1期2012卷

作者： X. Zhang G. Foderaro C. Henriquez A. M. J. VanDongen S. Ferrari Laboratory for Intelligent Systems and Control (LISC) Department of Mechanical Engineering and Materials Science Duke University Durham NC 27708 USAduke.edu Department of Biomedical Engineering and Department of Computer Science Duke University Durham NC 27708 USAduke.edu Program in Neuroscience & Behavioral Disorders Duke-NUS Graduate Medical School Singapore Singapore

This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Assessing Directed Information as a Method for Inferring Functional Connectivity in Neural Ensembles

Assessing Directed Information as a Method for Inferring Fun...

引用

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

作者： Kelvin So Michael Gastpar Jose M. Carmena Department of Electrical Engineering and Computer Sciences University of California Department of Electrical Engineering and Computer Sciences the Program in Cognitive Science and the Helen Wills Neuroscience Institute University of California

ISBN: (纸本)9781424441211

Neurons in the brain form complicated networks through synaptic connections. Traditionally, functional connectivity between neurons has been analyzed using simple metrics such as correlation, which do not provide direction of influence. Recently, an information theoretic measure known as directed information has been proposed as a way to capture directionality in the relationship, thereby moving towards a model of effective connectivity. This measure is grounded upon the concept of Granger causality and can be estimated by modeling neural spike trains as point process generalized linear models. However, the added benefit of using directed information to infer connectivity over conventional methods such as correlation is still unclear. Here, we propose a novel estimation procedure for the directed information. Using physiologically realistic simulations, we demonstrate that directed information can outperform correlation in determining connections between neural spike trains while also providing directionality of the relationship, which cannot be assessed using correlation.

关键词： Neurons Correlation Topology Computational modeling Accuracy Brain modeling Measurement

来源：评论

学校读者我要写书评

暂无评论

Exploring Time-Scales of Closed-Loop Decoder Adaptation in Brain-Machine Interfaces

Exploring Time-Scales of Closed-Loop Decoder Adaptation in B...

引用

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

作者： Amy L. Orsborn Siddharth Dangi Helene G. Moorman Jose M. Carmena Berkeley - Unviversity of California Department of Electrical Engineering and Computer Sciences University of California Helen Wills Neuroscience Institute University of California Department of Electrical Engineering and Computer Sciences Helen Wills Neuroscience Institute and Program in Cognitive Science University of California

ISBN: (纸本)9781424441211

Performing closed-loop modifications of a brain-machine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that our experimental training models are representative of a broad range of paralyzed patients, our decoders were initially trained using neural activity recorded during subject observation of cursor movement. We find that both closed-loop adaptation algorithms can be used to boost BMI performance from 20-30% to 80%, yielding movement kinematics similar to natural arm movements. Based on insights derived from the performance of each algorithm, we propose that a hybrid of batch and online decoder adaptation may be the best approach.

关键词： Decoding Kinematics Training Educational institutions Adaptation models Kalman filters USA Councils

来源：评论

学校读者我要写书评

暂无评论

From market baskets to mole rats: Using data mining techniques to analyze RFID data describing mole rat behavior 11

From market baskets to mole rats: Using data mining techniqu...

引用

17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011

作者： McCloskey, Dan P. Kress, Michael E. Imberman, Susan P. Kushnir, Igor Briffa-Mirabella, Susan Dept. Psychology College of Staten Island 2800 Victory Blvd Staten Island NY 10314 United States CSI/IBR Center for Developmental Neuroscience College of Staten Island 2800 Victory Blvd Staten Island NY 10314 United States College of Staten Island Department of Technology Systems 2800 Victory Blvd Staten Island NY 10314 United States College of Staten Island Dept. Computer Science 2800 Victory Blvd Staten Island NY 10314 United States College of Staten Island Masters Program In Neuroscience 2800 Victory Blvd Staten Island NY 10314 United States Program in Neuroscience Department of Biology Graduate Center at CUNY 2800 Victory Blvd Staten Island NY 10314 United States Program Computer Science Graduate Center at CUNY College of Staten Island 2800 Victory Blvd Staten Island NY 10314 United States

ISBN: (纸本)9781450308137

The use of new technologies, such as RFID sensors, provides scientists with novel ways of doing experimental research. As scientists become more technologically savvy and use these techniques, the traditional approaches to data analysis fail given the huge amounts of data produced by these methods. In this paper we describe an experiment in which colonies of naked mole rats were tagged with RFID transponders. RFID sensors were strategically placed in the mole rat caging system. The goal of this experiment was to document and analyze the interactions between animals. The huge amount of data produced by the sensors was not analyzable using the traditional methods employed by behavioral neuroscience researchers. Computational methods used by data miners, such as cluster analysis, association rule mining, and graphical models, were able to scale to the data and produce knowledge and insight that was previously unknown. This paper describes in detail the experimental setup and the computational methods used. Copyright 2011 ACM.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

Online correction of orientation estimates using spatial memory in a neuromorphic head direction system

Online correction of orientation estimates using spatial mem...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tarek M. Massoud Timothy K. Horiuchi Electrical and Computer Engineering Department Institute of Systems Research University of Maryland College Park MD USA Electrical and Computer Engineering Department Institute of Systems Research Neuroscience and Cognitive Science Program University of Maryland College Park MD USA

Many animals are known to maintain an internal estimate of their orientation in the environment. In the absence of external sensory cues, this estimate inevitably exhibits drift. When sensory information is available, associations between sensory landmarks and the internal estimate can be used to correct for drift. In this paper we present a neuromorphic system to model such associations between sensory landmarks in the environment (as provided by sonar) and the activity of a hardware-based head direction cell system (HDS) that continuously integrates angular velocity signals to maintain an estimate of the orientation. These associations are shown to correct for drift errors that are encountered in the HDS.

关键词： High definition video Robot sensing systems Neurons Navigation Sonar detection Neuromorphics

来源：评论

学校读者我要写书评

暂无评论

Adaptive Kalman filtering for closed-loop Brain-Machine Interface systems

Adaptive Kalman filtering for closed-loop Brain-Machine Inte...

引用

International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Siddharth Dangi Suraj Gowda Rodolphe Héliot Jose M. Carmena Department of Electrical Engineering and Computer Sciences University of California Berkeley CA USA CEA-LETI France Department of Electrical Engineering and Computer Sciences Helen Wills Neuroscience Institute and Program in Cognitive Science University of California Berkeley CA USA

Brain-Machine Interface (BMI) decoding algorithms are often trained offline, but this paradigm ignores both the non-stationarity of neural signals and the feedback that exists in online, closed-loop control. To address these problems, we have developed an Adaptive Kalman Filter (AKF), a Kalman filter variant that adaptively updates its model parameters during training. For a Kalman filter decoder, batch retraining methods require completely re-estimating the parameter matrices from sufficient data to perform regression accurately, even if only small changes are necessary. Conversely, the AKF is designed to update the decoder parameters continuously and more intelligently. We simulated a population of 41 neurons learning to control a 2D computer cursor. The AKF yielded significantly faster skill acquisition and better robustness to perturbation and neuron loss than a standard Kalman filter with periodic batch retraining.

关键词： Decoding Kalman filters Mathematical model Equations Kinematics Neurons Training

来源：评论

学校读者我要写书评

暂无评论

Redundant information encoding in primary motor cortex during motor tasks

Redundant information encoding in primary motor cortex durin...

引用

International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Kelvin So Karunesh Ganguly Michael Gastpar Jose M. Carmena Department of Electrical Engineering and Computer Sciences University of California Berkeley CA USA Neurology and Rehabilitation Services San Francisco Veterans Affairs Medical Center San Francisco CA USA Department of Electrical Engineering and Computer Sciences and the Helen Wills Neuroscience Institute University of California Berkeley CA USA Department of Electrical Engineering and Computer Sciences the Program in Cognitive Science and the Helen WillsNeuroscience Institute University of California Berkeley CA USA

Information encoded in neuron ensembles has previously been hypothesized to be highly redundant, despite the apparent inefficiency of a redundant encoding system. The recent availability of intracortical, multi-electrode recordings has enabled the possibility of exploring how neuronal ensembles encode information as a whole. Applying concepts from information theory, we examined the redundancy of the target information encoded in both contralateral and ipsilateral hemispheres of the primary motor cortex (M1) in macaque monkeys performing a center-out reaching task. During movement, we reliably found neurons in contralateral M1 to contain higher target information and to be more redundant than ipsilateral M1 neurons, which is consistent with the conventional understanding that motor control is mainly governed by contralateral M1. Secondly, neuron ensembles in both hemispheres showed largely redundant information encoding. These results suggest that redundancy in information encoding is highly prevalent in the motor cortex and may contribute to proficient motor control.

关键词： Neurons Redundancy Encoding neuroscience Muscles Firing Mutual information

来源：评论

学校读者我要写书评

暂无评论

Spiking neural network decoder for brain-machine interfaces

Spiking neural network decoder for brain-machine interfaces

引用

International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Julie Dethier Vikash Gilja Paul Nuyujukian Shauki A. Elassaad Krishna V. Shenoy Kwabena Boahen Department of Bioengineering University of Stanford Stanford CA USA Department of Computer Science and Stanford Institute of Neuro-Innovation and Translational Neuroscience University of Stanford Stanford CA USA Department of Bioengineering and MSTP University of Stanford Stanford CA USA Departments of Electrical Engineering and Bioengineering Neurosciences Program University of Stanford Stanford CA USA

We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations - neuromorphic chips - may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.

关键词： Neurons Kalman filters Decoding Prosthetics neuroscience Real time systems Neuromorphics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：