# The 2013 International Joint Conference on Neural Networks (IJCNN)

## Filter Results

Displaying Results 1 - 25 of 438
• ### [Title page]

Publication Year: 2013, Page(s):1 - 2
| PDF (51 KB)
• ### [Copyright notice]

Publication Year: 2013, Page(s): 1
| PDF (60 KB)
• ### Approximate dynamic programming solutions of multi-agent graphical games using actor-critic network structures

Publication Year: 2013, Page(s):1 - 8
Cited by:  Papers (5)
| | PDF (1207 KB) | HTML

This paper studies a new class of multi-agent discrete-time dynamical graphical games, where interactions between agents are restricted by a communication graph structure. The paper brings together discrete Hamiltonian mechanics, optimal control theory, cooperative control, game theory, reinforcement learning, and neural network structures to solve the multi-agent dynamical graphical games. Graphi... View full abstract»

• ### Behavioral economics and neuroeconomics: Cooperation, competition, preference, and decision making

Publication Year: 2013, Page(s):1 - 5
| | PDF (173 KB) | HTML

Behavioral economics and neuroeconomics concern how humans process multiple alternatives to make their decisions, and propose how discoveries about how the brain works can inform models of economic behavior. This lecture will survey how results about cooperative-competitive and cognitive-emotional dynamics that were discovered to better understand how brains control behavior can shed light on issu... View full abstract»

• ### John Gerald Taylor: Comrade, polymath, and consummate neuroscientist — Reminiscences of life together under the Aegis of INNS

Publication Year: 2013, Page(s):1 - 3
| | PDF (168 KB) | HTML

The author reviews the career of the late neural network pioneer John Taylor, including reminiscences of twenty years of close professional association and friendship. View full abstract»

• ### John Taylor, learning, attention, and consciousness

Publication Year: 2013, Page(s):1 - 3
| | PDF (121 KB) | HTML

This invited talk will begin with an appreciation of John Taylor and then present a review of classical and recent research results on topics that were of particular interest to John Taylor, notably neural models that contribute to our understanding of how attention and consciousness work. This extended abstract summarizes some of the main themes of the talk. View full abstract»

• ### The race to new mathematics of brains and consciousness — A tribute to John G. Taylor

Publication Year: 2013, Page(s):1 - 3
| | PDF (170 KB) | HTML

This contribution presents a review of mathematical approaches to modeling brains and higher cognitive activity, including consciousness. We dedicate this paper to John Gerard Taylor on the somber occasion of remembering his lifelong contribution to science, with a focus on his pioneering work on neural networks and brain studies. View full abstract»

• ### Cognitive models of the perception-action cycle: A view from the brain

Publication Year: 2013, Page(s):1 - 8
Cited by:  Papers (2)
| | PDF (410 KB) | HTML

Perception-action cycle is the circular flow of information that takes place between an organism and its environment in the course of a sensory-guided sequence of actions towards a goal. Each action causes changes in the environment which are processed by the organism's sensory hierarchy and lead to the generation of further action by its motor effectors. These actions cause new changes that are s... View full abstract»

• ### In search of the brain's executive

Publication Year: 2013, Page(s):1 - 5
| | PDF (229 KB) | HTML

In unpublished work over about ten years starting in 1995, John Taylor, Raju Bapi, Guido Bugmann, Neill Taylor, and I developed a network theory of the cognitive functions of the frontal lobes. We dealt with the long popular notion of the prefrontal region as executive of the brain [1]. Some cognitive neuroscientists had called for abandoning the executive idea because the functions of planning, o... View full abstract»

• ### Toward a cooperative brain: Continuing the work with John Taylor

Publication Year: 2013, Page(s):1 - 5
| | PDF (942 KB) | HTML

I propose a three-step discussion following a research path shared in part with John Taylor where the leitmotif is to understand the cooperation between thinking agents: the pRAM architecture, the butler paradigm, and the networked intelligence. All three steps comprise keystones of European projects which one of us has coordinated. The principled philosophy is to “start simple and insert progress... View full abstract»

• ### Cognitive computation: A case study in cognitive control of autonomous systems and some future directions

Publication Year: 2013, Page(s):1 - 6
Cited by:  Papers (2)
| | PDF (3150 KB) | HTML

Cognitive computation is an emerging discipline linking together neurobiology, cognitive psychology and artificial intelligence. Springer Neuroscience has launched a journal in this exciting multidisciplinary topic, which seeks to publish biologically inspired theoretical, computational, experimental and integrative accounts of all aspects of natural and artificial cognitive systems. In this keyno... View full abstract»

• ### On the interplay between “learning, memory, prospection and abstraction” in cumulatively learning baby humanoids

Publication Year: 2013, Page(s):1 - 10
Cited by:  Papers (1)
| | PDF (1711 KB) | HTML

We all inhabit' continuously changing unstructured worlds where neither everything can be known nor can everything be experienced. The lifelong interplay between neural mechanisms associated with learning, memory, prospection and abstraction play a fundamental role in enabling cognitive agents to effortlessly connect their past' with the available present' and possible future', most often in t... View full abstract»

• ### Bubbles in the robot

Publication Year: 2013, Page(s):1 - 2
| | PDF (289 KB) | HTML

Dynamic Neural Field models have been used extensively to model brain functions, but mostly through computer simulations. However, there are recent examples of applications in robotics that I will discuss in this presentation. I will also discuss neurocognitive robotics that has the aim of understanding brain functions in contrast to neuromorphic robotics that has mainly the aim of solving robotic... View full abstract»

• ### Point cloud data filtering and downsampling using growing neural gas

Publication Year: 2013, Page(s):1 - 8
Cited by:  Papers (7)
| | PDF (1198 KB) | HTML

3D sensors provide valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and ... View full abstract»

• ### A semi-parametric approach for football video annotation

Publication Year: 2013, Page(s):1 - 7
| | PDF (1515 KB) | HTML

Automatic sports video segmentation is a fast growth area of research in the visual information retrieval field. This paper presents a semi-parametric algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus performs a fast automatic football video annotation by ... View full abstract»

• ### Alignment-based transfer learning for robot models

Publication Year: 2013, Page(s):1 - 7
Cited by:  Papers (15)
| | PDF (1147 KB) | HTML

Robot manipulation tasks require on robot models. When exact physical parameters of the robot are not available, learning robot models from data becomes an appealing alternative. Most learning approaches are formulated in a supervised learning framework and are based on clearly defined training sets. We propose a method that improves the learning process by using additional data obtained from othe... View full abstract»

• ### Novelty estimation in developmental networks: Acetylcholine and norepinephrine

Publication Year: 2013, Page(s):1 - 8
| | PDF (1160 KB) | HTML

The receiver operating characteristic (ROC) curve has been widely applied to classifiers to show how the threshold value for acceptance changes the true positive rate and the false positive rate of the detection jointly. However, it is largely unknown how a biological brain autonomously selects a confidence value for each detection case. In the reported work, we investigated this issue based on th... View full abstract»

• ### Learning topological image transforms using cellular simultaneous recurrent networks

Publication Year: 2013, Page(s):1 - 9
Cited by:  Papers (3)
| | PDF (194 KB) | HTML

In this work, we investigate cellular simultaneous recurrent networks (CSRNs) to learn topological image mappings, particularly those of the affine transformations. While affine image transformation in conventional image processing is a relatively simple task, learning these transformations is an important part of having neural networks (NNs) function as generalized image processors. We introduce ... View full abstract»

• ### Human behaviour recognition based on trajectory analysis using neural networks

Publication Year: 2013, Page(s):1 - 7
Cited by:  Papers (7)
| | PDF (1124 KB) | HTML

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Descr... View full abstract»

• ### An incremental self-organizing neural network based on enhanced competitive Hebbian learning

Publication Year: 2013, Page(s):1 - 8
| | PDF (2401 KB) | HTML

Self-organizing neural networks are important tools for realizing unsupervised learning. Recently, a difficult task has involved the incremental, efficient and robust learning in noisy environments. Most of the existing techniques are poor in this regard. In this paper, we first propose a new topology generating method called enhanced competitive Hebbian learning (enhanced CHL), and then propose a... View full abstract»

• ### Clustering the self-organizing map through the identification of core neuron regions

Publication Year: 2013, Page(s):1 - 8
Cited by:  Papers (2)
| | PDF (1811 KB) | HTML

This paper presents an automatic clustering algorithm applied to SOM neurons. In the proposed method, every neuron has associated with it a weight and a feature vector, where the latter contains information of local density and local distances. The neurons are able to move in the SOM output grid so as to reach positions related to small pairwise distance among neurons and high density of patterns,... View full abstract»

• ### Hierarchical SOM-based detection of novel behavior for 3D human tracking

Publication Year: 2013, Page(s):1 - 8
Cited by:  Papers (5)
| | PDF (2059 KB) | HTML

We present a hierarchical SOM-based architecture for the detection of novel human behavior in indoor environments. The system can unsupervisedly learn normal activity and then report novel behavioral patterns as abnormal. The learning stage is based on the clustering of motion with self-organizing maps. With this approach, no domain-specific knowledge on normal actions is required. During the trac... View full abstract»

• ### Clustering iOS executable using self-organizing maps

Publication Year: 2013, Page(s):1 - 8
Cited by:  Papers (2)
| | PDF (135 KB) | HTML

We pioneer the study on applying both SOMs and GHSOMs to cluster mobile apps based on their behaviors, showing that the SOM family works well for clustering samples with more than ten thousands of attributes. The behaviors of apps are characterized by system method calls that are embedded in their executable, but may not be perceived by users. In the data preprocessing stage, we propose a novel st... View full abstract»

• ### Batch self-organizing maps for mixed feature-type symbolic data

Publication Year: 2013, Page(s):1 - 8
| | PDF (164 KB) | HTML

The Kohonen Self-Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. In this paper, we present batch SOM algorithms based on adaptive and non-adaptive distances for mixed feature-type symbolic data that, for a fixed epoch, optimize a cost function. The performance, and usefulness of these SOM alg... View full abstract»

• ### Modified self-organizing mixture network for probability density estimation and classification

Publication Year: 2013, Page(s):1 - 6
| | PDF (285 KB) | HTML

In this paper, a modified algorithm based on the Self-organizing Mixture Network (SOMN) is proposed to learn arbitrarily complex density functions accurately and effectively. The algorithm is derived based on the criterion of minimizing the Kullback-Leibler divergence, maximum likelihood approach and self-organizing principle. It has the advantages of stochastic approximation method such as fewer ... View full abstract»