Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033456
P. Arena, L. Patané, P. S. Termini
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.
{"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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033364
Atsushi Miyamoto, Kazuho Watanabe, K. Ikeda, Masa-aki Sato
Diffuse optical tomography is a method used to reconstruct tomographic images from brain activities observed by near-infrared spectroscopy. This is useful for brain-machine interface and is formulated as an ill-posed inverse problem. We apply a hierarchical Bayesian approach, automatic relevance determination (ARD) prior and the variational Bayes method, that can introduce localization into the estimation of the problem. Although ARD enables sparse estimation, it is still open how hyperparameters affect the sparseness and accuracy of the estimation. Through numerical experiments, we present a schematic phase diagram of sparseness with respect to the hyperparameters in the method, which indicates the region of the hyperparameters where sparse estimation is achievable.
{"title":"Phase diagrams of a variational Bayesian approach with ARD prior in NIRS-DOT","authors":"Atsushi Miyamoto, Kazuho Watanabe, K. Ikeda, Masa-aki Sato","doi":"10.1109/IJCNN.2011.6033364","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033364","url":null,"abstract":"Diffuse optical tomography is a method used to reconstruct tomographic images from brain activities observed by near-infrared spectroscopy. This is useful for brain-machine interface and is formulated as an ill-posed inverse problem. We apply a hierarchical Bayesian approach, automatic relevance determination (ARD) prior and the variational Bayes method, that can introduce localization into the estimation of the problem. Although ARD enables sparse estimation, it is still open how hyperparameters affect the sparseness and accuracy of the estimation. Through numerical experiments, we present a schematic phase diagram of sparseness with respect to the hyperparameters in the method, which indicates the region of the hyperparameters where sparse estimation is achievable.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"93 10 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":"128663616","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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033447
D. Araújo, A. Neto, A. Martins, J. Melo
This paper proposes a study on the impact of the use of dimension reduction techniques (DRTs) in the quality of partitions produced by cluster analysis of microarray datasets. We tested seven DRTs applied to four microarray cancer datasets and ran four clustering algorithms using the original and reduced datasets. Overall results showed that using DRTs provides a improvement in performance of all algorithms tested, specially in the hierarchical class. We could see that, despite Principal Component Analysis (PCA) being the most widely used DRT, its was overcome by other nonlinear methods and it did not provide a substantial performance increase in the clustering algorithms. On the other hand, t-distributed Stochastic Embedding (t-SNE) and Laplacian Eigenmaps (LE) achieved good results for all datasets.
{"title":"Comparative study on dimension reduction techniques for cluster analysis of microarray data","authors":"D. Araújo, A. Neto, A. Martins, J. Melo","doi":"10.1109/IJCNN.2011.6033447","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033447","url":null,"abstract":"This paper proposes a study on the impact of the use of dimension reduction techniques (DRTs) in the quality of partitions produced by cluster analysis of microarray datasets. We tested seven DRTs applied to four microarray cancer datasets and ran four clustering algorithms using the original and reduced datasets. Overall results showed that using DRTs provides a improvement in performance of all algorithms tested, specially in the hierarchical class. We could see that, despite Principal Component Analysis (PCA) being the most widely used DRT, its was overcome by other nonlinear methods and it did not provide a substantial performance increase in the clustering algorithms. On the other hand, t-distributed Stochastic Embedding (t-SNE) and Laplacian Eigenmaps (LE) achieved good results for all datasets.","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":"128675875","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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033621
S. M. M. Vaghefi, S. M. M. Vaghefi
A novel multilayer neural network was designed and implemented for prediction of the hardness of electroless Ni-P coatings. Heat treatment, a process for adjusting the hardness of electroless Ni-P coatings, was modeled. Three neural network models, a multilayer preceptron, a radial basis functions network, and a novel model, called the decomposer-composer model, were implemented and applied to the problem. The input parameters were the phosphorus content of the coatings, and the temperature and duration of the heat treatment process. The models output was the hardness of electroless Ni-P coatings. The training and test data were extracted from a number of experimental projects. The decomposer-composer model achieved better result and performance compared to the other models.
{"title":"A novel multilayer neural network model for heat treatment of electroless Ni-P coatings","authors":"S. M. M. Vaghefi, S. M. M. Vaghefi","doi":"10.1109/IJCNN.2011.6033621","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033621","url":null,"abstract":"A novel multilayer neural network was designed and implemented for prediction of the hardness of electroless Ni-P coatings. Heat treatment, a process for adjusting the hardness of electroless Ni-P coatings, was modeled. Three neural network models, a multilayer preceptron, a radial basis functions network, and a novel model, called the decomposer-composer model, were implemented and applied to the problem. The input parameters were the phosphorus content of the coatings, and the temperature and duration of the heat treatment process. The models output was the hardness of electroless Ni-P coatings. The training and test data were extracted from a number of experimental projects. The decomposer-composer model achieved better result and performance compared to the other models.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"34 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":"129572369","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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033644
Y. Kuroe, H. Kawakami
The problem of recovering shape from shading is important in computer vision and robotics. In this paper, we propose a versatile method of solving the problem by neural networks. We introduce a mathematical model, which we call ‘image-formation model’, expressing the process that the image is formed from an object surface. We formulate the problem as a model inclusive learning problem of neural networks and propose a method to solve it. In the proposed learning method, the image-formation model is included in the learning loop of neural networks. The proposed method is versatile in the sense that it can solve the problem in various circumstances. The effectiveness of the proposed method is shown through experiments performed in various circumstances.
{"title":"Versatile neural network method for recovering shape from shading by model inclusive learning","authors":"Y. Kuroe, H. Kawakami","doi":"10.1109/IJCNN.2011.6033644","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033644","url":null,"abstract":"The problem of recovering shape from shading is important in computer vision and robotics. In this paper, we propose a versatile method of solving the problem by neural networks. We introduce a mathematical model, which we call ‘image-formation model’, expressing the process that the image is formed from an object surface. We formulate the problem as a model inclusive learning problem of neural networks and propose a method to solve it. In the proposed learning method, the image-formation model is included in the learning loop of neural networks. The proposed method is versatile in the sense that it can solve the problem in various circumstances. The effectiveness of the proposed method is shown through experiments performed in various circumstances.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"05 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":"127352191","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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033291
Sepideh Sadeghi, K. Ramanathan
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.
{"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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033432
Talitha Rubio, Tiantian Zhang, M. Georgiopoulos, Assem Kaylani
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.
{"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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033307
D. Reyes, T. Baidyk, E. Kussul
For robot navigation (obstacle avoidance) we propose to use special neural network, because of its large information capacity for non correlated data. We prove this feature in contrast for correlated data in the robot task. This information is generated by a simulator and coded into neural ensembles. The coding method allows different parameters with their numeric values to be stored; it also provides similarity for close values and eliminates it in other case. The developed system combines the quality of the neural network as associative memory and the coding method to permit learning from some specific situations. So we prove the system introducing only the situation information and retrieving the appropriate maneuver for it.
{"title":"Information coding with neural ensembles for a mobile robot","authors":"D. Reyes, T. Baidyk, E. Kussul","doi":"10.1109/IJCNN.2011.6033307","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033307","url":null,"abstract":"For robot navigation (obstacle avoidance) we propose to use special neural network, because of its large information capacity for non correlated data. We prove this feature in contrast for correlated data in the robot task. This information is generated by a simulator and coded into neural ensembles. The coding method allows different parameters with their numeric values to be stored; it also provides similarity for close values and eliminates it in other case. The developed system combines the quality of the neural network as associative memory and the coding method to permit learning from some specific situations. So we prove the system introducing only the situation information and retrieving the appropriate maneuver for it.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"37 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":"129298815","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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033510
Y. Choe, D. Mayerich, Jaerock Kwon, Daniel E. Miller, Ji Ryang Chung, C. Sung, J. Keyser, L. Abbott
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.
在本文中，我们将回顾一种新的显微镜模式，称为刀口扫描显微镜(KESM)，我们已经发展了12年(自1999年以来)，并讨论其与连接组学和神经网络研究的相关性。KESM的工作原理是同时对嵌入在硬聚合物树脂中的小动物大脑进行切片和成像，以便在约1 cm3的组织体积上实现接近各向同性的亚微米体素尺寸(0.6 μ m × 0.7 μ m × 1.0 μ m)，足以容纳整个小鼠大脑。在这个分辨率下，可以看到树突、树突棘和轴突等形态学细节(对于像高尔基体这样的稀疏斑点)。KESM已成功用于扫描用高尔基体(神经元形态学)、尼氏体(体细胞)和印度墨水(脉管系统)染色的整个小鼠大脑，为小鼠大脑内微结构的系统级结构布局提供了前所未有的见解。在本文中，我们将展示来自KESM的全脑规模数据集，并讨论这些详细的系统级数据给连接组学和神经网络研究带来的挑战和机遇。
{"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}
Pub Date : 2011-10-03DOI: 10.1109/IJCNN.2011.6033501
A. D. Nuovo, D. Marocco, S. Nuovo, A. Cangelosi
Understanding the process behind the human ability of creating mental images of events and experiences is a still crucial issue for psychologists. Mental imagery may be considered a multimodal biological simulation that activates the same, or very similar, sensorial and motor modalities that are activated when we interact with the environment in real time. Neuro-psychological studies show that neural mechanisms underlying real-time visual perception and mental visualization are the same when a task is mentally recalled. Nevertheless, the neural mechanisms involved in the active elaboration of mental images might be different from those involved in passive elaborations. The enhancement of this active and creative imagery is the aim of most psychological and educational processes, although, more empirical effort is needed in order to understand the mechanisms and the role of active mental imagery in human cognition. In this work we present some results of on ongoing investigation about mental imagery using cognitive robotics. Here we focus on the capability to estimate, from proprioceptive and visual information, the position into a soccer field when the robot acquires the goal. Results of simulation with the iCub platform are given to show that the computational model is able to efficiently estimate the robot's position. The final objective of our work is to replicate with a cognitive robotics model the mental imagery when it is used during the training phase of athletes that are allowed to imaginary practice to score a goal.
{"title":"A Neural Network model for spatial mental imagery investigation: A study with the humanoid robot platform iCub","authors":"A. D. Nuovo, D. Marocco, S. Nuovo, A. Cangelosi","doi":"10.1109/IJCNN.2011.6033501","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033501","url":null,"abstract":"Understanding the process behind the human ability of creating mental images of events and experiences is a still crucial issue for psychologists. Mental imagery may be considered a multimodal biological simulation that activates the same, or very similar, sensorial and motor modalities that are activated when we interact with the environment in real time. Neuro-psychological studies show that neural mechanisms underlying real-time visual perception and mental visualization are the same when a task is mentally recalled. Nevertheless, the neural mechanisms involved in the active elaboration of mental images might be different from those involved in passive elaborations. The enhancement of this active and creative imagery is the aim of most psychological and educational processes, although, more empirical effort is needed in order to understand the mechanisms and the role of active mental imagery in human cognition. In this work we present some results of on ongoing investigation about mental imagery using cognitive robotics. Here we focus on the capability to estimate, from proprioceptive and visual information, the position into a soccer field when the robot acquires the goal. Results of simulation with the iCub platform are given to show that the computational model is able to efficiently estimate the robot's position. The final objective of our work is to replicate with a cognitive robotics model the mental imagery when it is used during the training phase of athletes that are allowed to imaginary practice to score a goal.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"29 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":"130693292","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}
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