The 2011 International Joint Conference on Neural Networks最新文献_第8页

Implementation of signal processing tasks on neuromorphic hardware 在神经形态硬件上实现信号处理任务

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033349

O. Temam, R. Héliot

引用次数: 7

An insect brain inspired neural model for object representation and expectation 昆虫大脑启发的对象表征和期望的神经模型

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033456

P. Arena, L. Patané, P. S. Termini

{"title":"An insect brain inspired neural model for object representation and expectation","authors":"P. Arena, L. Patané, P. S. Termini","doi":"10.1109/IJCNN.2011.6033456","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033456","url":null,"abstract":"In spite of their small brain, insects show a complex behavior repertoire and are becoming a reference point in neuroscience and robotics. In particular, it is very interesting to analyze how biological reaction-diffusion systems are able to codify sensorial information with the addition of learning capabilities. In this paper we propose a new model of the olfactory system of the fruit fly Drosophila melanogaster. The architecture is a multi-layer spiking neural network, inspired by the structures of the insect brain mainly involved in the olfactory conditioning, namely the Mushroom Bodies, the Lateral Horns and the Antennal Lobes. The Antennal Lobes model is based on a competitive topology that transduces the sensorial information into a pattern, projecting such information to the Mushroom Bodies model. This model is based on a first and second order reaction-diffusion paradigm that leads to a spontaneous emerging of clusters. The Lateral Horns have been modeled as an input-triggered resetting system. The structure, besides showing the already known capabilities of associative learning, via a bottom-up processing, is also able to realize a top-down modulation at the input level, in order to implement an expectation-based filtering of the sensorial inputs.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128608890","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: 7

Phase diagrams of a variational Bayesian approach with ARD prior in NIRS-DOT NIRS-DOT中具有ARD先验的变分贝叶斯方法的相图

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033364

Atsushi Miyamoto, Kazuho Watanabe, K. Ikeda, Masa-aki Sato

引用次数: 4

Comparative study on dimension reduction techniques for cluster analysis of microarray data 微阵列数据聚类分析降维技术的比较研究

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033447

D. Araújo, A. Neto, A. Martins, J. Melo

引用次数: 15

A novel multilayer neural network model for heat treatment of electroless Ni-P coatings 一种新的Ni-P化学镀层热处理多层神经网络模型

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033621

S. M. M. Vaghefi, S. M. M. Vaghefi

引用次数: 2

Versatile neural network method for recovering shape from shading by model inclusive learning 基于模型包容学习的阴影形状恢复的通用神经网络方法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033644

Y. Kuroe, H. Kawakami

引用次数: 7

A Hubel Wiesel model of early concept generalization based on local correlation of input features 基于输入特征局部相关的早期概念泛化Hubel Wiesel模型

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033291

Sepideh Sadeghi, K. Ramanathan

{"title":"A Hubel Wiesel model of early concept generalization based on local correlation of input features","authors":"Sepideh Sadeghi, K. Ramanathan","doi":"10.1109/IJCNN.2011.6033291","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033291","url":null,"abstract":"Hubel Wiesel models, successful in visual processing algorithms, have only recently been used in conceptual representation. Despite the biological plausibility of a Hubel-Wiesel like architecture for conceptual memory and encouraging preliminary results, there is no implementation of how inputs at each layer of the hierarchy should be integrated for processing by a given module, based on the correlation of the features. In our paper, we propose the input integration framework - a set of operations performed on the inputs to the learning modules of the Hubel Wiesel model of conceptual memory. These operations weight the modules as being general or specific and therefore determine how modules can be correlated when fed to parents in the higher layers of the hierarchy. Parallels from Psychology are drawn to support our proposed framework. Simulation results on benchmark data show that implementing local correlation corresponds to the process of early concept generalization to reveal the broadest coherent distinctions of conceptual patterns. Finally, we applied the improved model iteratively over two sets of data, which resulted in the generation of finer grained categorizations, similar to progressive differentiation. Based on our results, we conclude that the model can be used to explain how humans intuitively fit a hierarchical representation for any kind of data.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129963995","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: 2

Multi-objective evolutionary optimization of exemplar-based classifiers: A PNN test case 基于样本分类器的多目标进化优化:一个PNN测试用例

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033432

Talitha Rubio, Tiantian Zhang, M. Georgiopoulos, Assem Kaylani

{"title":"Multi-objective evolutionary optimization of exemplar-based classifiers: A PNN test case","authors":"Talitha Rubio, Tiantian Zhang, M. Georgiopoulos, Assem Kaylani","doi":"10.1109/IJCNN.2011.6033432","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033432","url":null,"abstract":"In this paper the major principles to effectively design a parameter-less, multi-objective evolutionary algorithm that optimizes a population of probabilistic neural network (PNN) classifier models are articulated; PNN is an example of an exemplar-based classifier. These design principles are extracted from experiences, discussed in this paper, which guided the creation of the parameter-less multi-objective evolutionary algorithm, named MO-EPNN (multi-objective evolutionary probabilistic neural network). Furthermore, these design principles are also corroborated by similar principles used for an earlier design of a parameter-less, multi-objective genetic algorithm used to optimize a population of ART (adaptive resonance theory) models, named MO-GART (multi-objective genetically optimized ART); the ART classifier model is another example of an exemplar-based classifier model. MO-EPNN's performance is compared to other popular classifier models, such as SVM (Support Vector Machines) and CART (Classification and Regression Trees), as well as to an alternate competitive method to genetically optimize the PNN. These comparisons indicate that MO-EPNN's performance (generalization on unseen data and size) compares favorably to the aforementioned classifier models and to the alternate genetically optimized PNN approach. MO-EPPN's good performance, and MO-GART's earlier reported good performance, both of whose design relies on the same principles, gives credence to these design principles, delineated in this paper.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129168704","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: 1

Information coding with neural ensembles for a mobile robot 移动机器人的神经集成信息编码

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033307

D. Reyes, T. Baidyk, E. Kussul

引用次数: 1

Knife-edge scanning microscopy for connectomics research 用于连接组学研究的刀口扫描显微镜

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033510

Y. Choe, D. Mayerich, Jaerock Kwon, Daniel E. Miller, Ji Ryang Chung, C. Sung, J. Keyser, L. Abbott

{"title":"Knife-edge scanning microscopy for connectomics research","authors":"Y. Choe, D. Mayerich, Jaerock Kwon, Daniel E. Miller, Ji Ryang Chung, C. Sung, J. Keyser, L. Abbott","doi":"10.1109/IJCNN.2011.6033510","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033510","url":null,"abstract":"In this paper, we will review a novel microscopy modality called Knife-Edge Scanning Microscopy (KESM) that we have developed over the past twelve years (since 1999) and discuss its relevance to connectomics and neural networks research. The operational principle of KESM is to simultaneously section and image small animal brains embedded in hard polymer resin so that a near-isotropic, sub-micrometer voxel size of 0.6 µm × 0.7 µm × 1.0 µm can be achieved over ∼1 cm3 volume of tissue which is enough to hold an entire mouse brain. At this resolution, morphological details such as dendrites, dendritic spines, and axons are visible (for sparse stains like Golgi). KESM has been successfully used to scan whole mouse brains stained in Golgi (neuronal morphology), Nissl (somata), and India ink (vasculature), providing unprecedented insights into the system-level architectural layout of microstructures within the mouse brain. In this paper, we will present whole-brain-scale data sets from KESM and discuss challenges and opportunities posed to connectomics and neural networks research by such detailed yet system-level data.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130663968","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: 14