2016 International Joint Conference on Neural Networks (IJCNN)最新文献_第5页

Relational Fisher Analysis: A general framework for dimensionality reduction 关系费雪分析:降维的一般框架

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727477

G. Zhong, Yaxin Shi, M. Cheriet

引用次数: 6

A novel homeostatic plasticity model realized by random fluctuations in excitatory synapses 一种由兴奋性突触随机波动实现的新稳态可塑性模型

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727315

Takashi Matsubara, K. Uehara

{"title":"A novel homeostatic plasticity model realized by random fluctuations in excitatory synapses","authors":"Takashi Matsubara, K. Uehara","doi":"10.1109/IJCNN.2016.7727315","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727315","url":null,"abstract":"Homeostatic plasticity in mammalian central nervous system is considered to maintain activity in neuronal circuits within a functional range. In the absence of homeostatic plasticity neuronal activity is prone to be destabilized because correlation-based synaptic modification, Hebbian plasticity, induces positive feedback change. Several studies on homeostatic plasticity assumed the existence of a process for monitoring neuronal activity and adjusting synaptic efficacy on a time scale of hours, but its biological mechanism still remains unclear. Excitatory synaptic efficacy is associated with the size of a post-synaptic element, dendritic spine, and the size of the dendritic spine fluctuates even after neuronal activity is silenced. These fluctuations could be a non-Hebbian form of synaptic plasticity that serves such a homeostatic function. This study proposed and analyzed a synaptic plasticity model incorporating random fluctuations and Hebbian plasticity at excitatory synapses, and found that it prevents excessive changes in neuronal activity by adjusting synaptic efficacy. Random fluctuations do not monitor neuronal activity, but their relative influence depends on neuronal activity. The proposed synaptic plasticity model acts as a form of homeostatic plasticity, regardless of neuronal activity monitoring. Thus, random fluctuations play an important role in homeostatic plasticity and contribute to development and functions of neural networks.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121944777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sonar-based place recognition using joint sparse coding method 基于声纳的位置识别联合稀疏编码方法

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727701

Xiangmei Zheng, Huaping Liu, F. Sun, Meng Gao, Jiakui Li, Qing Zhang

引用次数: 3

On the energy benefits of spiking deep neural networks: A case study 关于脉冲深度神经网络的能量效益:一个案例研究

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727303

Bing Han, Abhronil Sengupta, K. Roy

引用次数: 30

Background modeling on depth video sequences using self-organizing retinotopic maps 基于自组织视网膜定位图的深度视频序列背景建模

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727319

M. Murguia, Oscar Alejandro Chavez-Montes, J. Ramirez-Quintana

引用次数: 1

Multiple adaptive kernel size KLMS for Beijing PM2.5 prediction 北京PM2.5预测的多自适应核大小KLMS

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727362

Zheng Cao, Shujian Yu, Guibiao Xu, Badong Chen, J. Príncipe

引用次数: 10

Hidden Markov model inference in an Associative memory architecture 联想记忆体系结构中的隐马尔可夫模型推理

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727458

J. Poikonen, M. Laiho, E. Lehtonen, T. Knuutila

引用次数: 0

Distributed variance regularized Multitask Learning 分布方差正则化多任务学习

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727594

Michele Donini, David Martínez-Rego, Martin Goodson, J. Shawe-Taylor, M. Pontil

引用次数: 5

A multiagent reinforcement learning approach to en-route trip building 一种基于多智能体强化学习的途中行程构建方法

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727899

A. Bazzan, Ricardo Grunitzki

{"title":"A multiagent reinforcement learning approach to en-route trip building","authors":"A. Bazzan, Ricardo Grunitzki","doi":"10.1109/IJCNN.2016.7727899","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727899","url":null,"abstract":"An important stage in traffic planning is traffic assignment, which seeks to reproduce the way drivers select their routes. It assumes that each driver is aware of a number of routes to travel from an origin to a destination, that it performs some experimentation, and that it selects rationally the route with the highest utility. This is the basis for many approaches that, in an iterative way, vary the combination of route choices in order to find one that maximizes the utility. This perspective is therefore a centralized, aggregate one. In reality, though, drivers may perform en-route experimentation, i.e., they deviate from the originally planned route. Thus, in this paper, individual drivers are considered as active and autonomous agents, which, instead of having a central entity assigning complete trips to each agent, build these trips by experimentation during the actual trip. Agents learn their routes by deciding, at each node, how to continue their trips to each one's destination, in a way to minimize their travel times. Because the choice of one agent does impact several others, this is a non-cooperative multiagent learning problem (thus stochastic), which is known for being much more challenging than single agent reinforcement learning. To illustrate this approach, results from two non-trivial networks are presented, which have thousands of learning agents, clearly configuring a hard learning problem. Results are compared to iterative, centralized methods. It is concluded that an agent-based perspective yields choices that are more aligned with the real-world situation because (i) trips are computed by the agent itself (and not provided to the agent by any central entity), and (ii) it is not based on pre-computed paths (rather, it is built during the trip itself).","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130109007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Feature leaning with deep Convolutional Neural Networks for screening patients with paroxysmal atrial fibrillation 深度卷积神经网络特征学习在阵发性房颤筛查中的应用

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727866

B. Pourbabaee, M. J. Roshtkhari, K. Khorasani

{"title":"Feature leaning with deep Convolutional Neural Networks for screening patients with paroxysmal atrial fibrillation","authors":"B. Pourbabaee, M. J. Roshtkhari, K. Khorasani","doi":"10.1109/IJCNN.2016.7727866","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727866","url":null,"abstract":"In this paper, a novel electrocardiogram (ECG) signal classification and patient screening method is developed. The focus is on identifying patients with paroxysmal atrial fibrillation (PAF) which is a life threatening cardiac arrhythmia. The proposed approach uses the raw ECG signal as the input and automatically learns the representative features for PAF to be used by a classification mechanism. The features are learned directly from the time domain ECG signals by using a Convolutional Neural Network (CNN) with one fully connected layer. The learned features can replace the hand-crafted features and our experimental results indicate the effectiveness of the learned features in patient screening. The experimental results indicate that combining the learned features with other classifiers will improve the performance of the patient screening system as compared to an End-to-End convolutional neural network classifier. The major characteristics of the proposed approach are to simplify the process of feature extraction for different cardiac arrhythmias and to remove the need for using a human expert to specify the appropriate features. The effectiveness of the proposed ECG classification method is demonstrated through performing extensive simulation studies.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28