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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 robotics...
Cognitive dissonance, CD, leads to discarding of contradictory knowledge. The presentation discusses that all knowledge is contradictory and according to CD theory should have been discarded in evolution before its usefulness would have been established. Therefore a powerful ability should have emerged along with language and diverse knowledge to overcome this aspect of CD. We present experimental...
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.
The author reviews the career of the late neural network pioneer John Taylor, including reminiscences of twenty years of close professional association and friendship.
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.
Early diagnosis of heart defects are very important for medical treatment. In this paper, we propose an automatic method to segment heart sounds, which applies classification and regression trees. The diagnostic system, designed and implemented for detecting and classifying heart diseases, has been validated with a representative dataset of 116 heart sound signals, taken from healthy and unhealthy...
This paper describes a neural network based equalizer trained by the artificial immune system learning algorithm. The equalizer takes advantage of the characteristics of neural nets and artificial immune systems. Simulations for channel responses examples indicate the usefulness of the proposed equalizer.
In this paper, the extended kernel recursive least squares (Ex-KRLS) algorithm is reviewed and analyzed. We point out that the Theorem 1 in [10] is not always correct for general cases. Furthermore, the Ex-KRLS algorithm for tracking model is just a random walk KRLS algorithm. Finally, this algorithm is explained as a special Kalman filter in the reproducing kernel Hilbert space.
In this paper, we propose a novel method of orientation analysis for seismic image. Unlike conventional methods of orientation estimation with a context window, our method is inspired by the neural mechanism of visual perception in the biological brain. In the brain, the primary visual cortex contains orientation columns, which is composed of an array of simple cells as orientation detectors. A log-Gabor...
This paper proposes a parameter-free kernel that is translation invariant and positive definite. The new kernel is based on the data cumulative distribution function (CDF) that provides all the statistical information about the observed samples. Without an explicit kernel size parameter, this novel kernel is used to define the autocorrentropy function, which is a generalized similarity measure, and...
A robust neural predictor is designed for noisy chaotic time series prediction in this paper. The main idea is based on the consideration of the bounded uncertainty in predictor input, and it is a typical Errors-in-Variables problem. The robust design is based on the linear-in-parameters ESN (Echo State Network) model. By minimizing the worst-case residual induced by the bounded perturbations in the...
Selecting attention is an important cognitive psychology concept originally which has received much attention from scholars in the field of computer science. Nowadays, selecting attention has much application in computer vision. Most current computational models of attention focus on bottom-up features and ignore scene information. In this paper, a model of selecting visual attention guidance based...
Most previous studies treated spikes as all-or-none events, and considered their duration and magnitude as negligible. Action potential (AP) duration varies across neuron types, but its consequences on synaptic plasticity remain largely unexplored. Here we study the effects of AP-duration on spike-timing dependent synaptic plasticity (STDP) by negatively shifting the temporal window, potentiating...
Multi-label classification is an extension of traditional classification problem in which each instance is associated with a set of labels. For some multi-label classification tasks, labels are usually overlapped and correlated, and some implicit constraint rules are existed among the labels. This paper presents an improved multi-label classification method based on label ranking strategy and label...
We present a traffic sign detection method which has won the first place for the prohibitory and mandatory signs and the third place for the danger signs in the GTSDB competition. The method uses the histogram of oriented gradient (HOG) and a coarse-to-fine sliding window scheme. Candidate ROIs are first roughly detected within a small-sized window, and then further verified within a large-sized window...
Bayesian Network (BN) is one of the most popular models in data mining technologies. Most of the algorithms of BN structure learning are developed for the centralized datasets, where all the data are gathered into a single computer node. They are often too costly or impractical for learning BN structures from large scale data. Through a simple interface with two functions, map and reduce, MapReduce...
The performance of layered neural networks with quaternionic encoding variables are investigated in this paper. The form of local analyticity with Wirtinger representation is adopted for a backpropagation learning algorithm in this network. A quaternionic version of tanh function is used for the activation function in neuron states' updates. As tasks of the performance evaluation of the presented...
This work exploits the idea on how to search parameter estimation and increase its convergence speed for the Liner Time Invariant (LTI) system. The convergence speed of parameter estimation is the one problem and plays an important role in the adaptive controller to increase performance. The well-known algorithm is the recursive least square algorithm. However, the speed of convergence is still low...
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