Antonia Plerou

Huntington’s disease as a neurodegenerative disease is characterized by motor and cognitive impairment. The disease is caused by the mutation of the gene that produces the huntingtin protein causing the repetition of trinucleotide CAG. The mutant protein reacts with other proteins inside and out of the cell causing problems to its normal function and cell death. Recent advances in the signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method able of providing spatio-temporal information regarding brain (dys)function. Authors aim to review objectively and quantitatively the neurophysiological basis of the disease in HD patients as compared to normal controls, with the use of brain imaging in general and EEG brain imaging methods.

During the last decades the interest about the research of neurocognitive issues within the frame of brain imaging techniques is considerably high. In this paper authors’ approach concerns the research and the evaluation of the so-called cognitive disorders within the frame of memory and algorithmic abilities. The exceptionality of calendrical savant’s skills, the influence of memory, calculation and algorithmic abilities were analyzed within the frame of cognitive neuroscience and brain imaging techniques. Future directions in order to enhance the theoretical findings concerning memory disorders – specifically by analyzing the brain activity signal within the use of brain imaging techniques are also discussed.

The fusion of Artificial Neural Networks and Fuzzy Logic Systems allows researchers to model real
world problems through the development of intelligent and adaptive systems. Artificial Neural networks
are able to adapt and learn by adjusting the interconnections between layers while fuzzy logic inference
systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and
fuzzy reasoning. The combined use of those adaptive structures is known as “Neuro-Fuzzy” systems. In
this chapter, the basic elements of both approaches are analyzed while neuro-fuzzy networks learning
algorithms are presented. Here, we combine the use of neuro-fuzzy algorithms with multimedia-based
signals for training. Ultimately this process may be employed for automatic identification of patterns
introduced in medical applications and more specifically for analysis of content produced by brain
imaging processes.

During the last decades, the interest displayed in neurocognitive and brain science research is relatively high. In this chapter, the cognitive neuroscience field approach focuses in the aspect of the way that cognitive functions are produced by neural circuits in the brain. Within this frame, the effects of impairment to the brain and subsequent changes in the thought processes due to changes in neural circuitry resulting from the ensued damage are analyzed and evaluated. All cognitive functions result from the integration of many simple processing mechanisms, distributed throughout the brain. Brain cortex structures, linked with cognitive disorders, are located in several parts like the frontal, the parietal, the temporal,
the occipital lobe and more are analyzed and specified. A critical topic of this chapter in the evaluation of brain operations is mapping regions that control cognitive and mathematical concepts functions.
Dyscalculia, in this chapter, is described as a specific disorder of managing and conceiving mathematical concepts. Dyscalculia could be identified by difficulties in visual perception, in spatial number organization, in basic mathematical operations and in mathematical induction logic. Moreover, people who deal with dyscalculia present problems, in Euclidean and Non-Euclidean Geometry concepts perception, in
Calculus aspects as well as in solving algorithmic problems where the design, the description and the application of algorithmic steps are required. In order to enhance cognitive brain functions perception, the use of EEG brain imaging is proposed measuring cerebral activity and event-related potentials. The procedure described in this chapter is about the comparison and contrasts EEG brain imaging patterns
of healthy volunteers to EEG samples taken of adults considered being at risk of mathematics learning disabilities such as Dyscalculia and algorithmic thinking difficulties. EEG interpretation analysis is to follow where the deviation of a normal and an abnormal range of wave’s frequency are defined. Several visualized
EEG patterns in relevance with specific abnormalities are presented while several neurocognitive generated disorders could be identified with the use of EEG Brain-imaging technique. The electroencephalogram EEG brain imaging procedure, in order to evaluate problems associated with brain function, is to be further analyzed in this chapter as well. The EEG is the depiction of the electrical activity occurring at the surface of the brain. The recorded waveforms reflect the cortical electrical
activity and they are generally classified according to their frequency (Delta, Theta, Beta, Alpha, Beta, and Gamma) amplitude, and shape. EEG Implementation with the use of 10/20 system of the standardized position of scalp electrodes placement for a classical EEG recording is described as well.
The EEG implementation objective is to identify, classify and evaluate those frequencies and regions in the brain that best characterize brain activity associated with mathematical learning disabilities. Mapping the brain with non-invasive techniques based on trigger and sensing/evaluation experimental multimedia methods similar to those used in computer games and applications are expected to provide relevant results in order to enhance and confirm theoretical cognitive aspects. At that point, a cognitive and mathematical perception evaluation is to follow and specifically the assessment of the relation of difficulties in mathematics with particular parts of the human brain. EEG wave data visualization is contacted with the use of Acknowledge an interactive, intuitive program which provides data analysis instantly. At the end of this chapter EEG computational evaluation with the use of pattern recognition methods as well as the intuition of author’s future work in relevance with the use of experimental multimedia technologies to enhance the dynamic recognition and evaluation of user cognitive responses during EEG implementation are noted.

The recent years the interest in neurocognitive and brain science research is relatively increased. In this study the relation of DNA and genetic factors that are interrelated to brain development and cognitive functions is analyzed. Author’s main purpose is to localize and analyze the function of the particular parts of brain which are related to specific cognitive functions. Especially the connection of specific brain cortexes with mathematical perception and mathematical learning difficulties also known as dyscalculia is analyzed and evaluated as well. Future directions in order to evaluate and enhance the theoretical outcomes by visualizing the brain activity within the use of brain imaging techniques are also discussed.

Although studies on learning difficulties are in general in advanced stage, research related to algorithmic thinking difficulties is limited, since interest in this field has been recently expressed. Additionally, young people nowadays are familiarized with the use of computer. Therefore, an interactive computer-based diagnostic screener, referring to dyscalculia and algorithmic thinking difficulties, is proposed. In this paper, a number of issues concerning algorithmic thinking are also explored. In particular, an approach of algorithmic thinking definition is given and difficulties in algorithmic thinking features are studied. People facing algorithmic thinking difficulties have common characteristic therefore it would be crucial to discover the way these features could be identified in order to detect people at risk. Precise and analytical conclusions concerning the efficiency of the proposed screener are to be reached upon completion and review of the overall project phases.

Although several surveys related to dyscalculia have proceeded so far, research seems to fail till now to develop a clear concept map referring to the features of dyscalculia classified according to age. Dyscalculia can be recognized by some specific features that are noticed through all stages of the individual's development. In this paper a classification of features is proposed, according to individuals' age as well as several screening methods, in order to ensure efficiency of the screening procedure. Dyscalculia's features evaluation during different stages of age is crucial since diagnosis is relevant to the age considering that math's learning difficulties arising in early ages are likely to be remitted since this status is possible to be temporary. An innovative aspect in this work is the citation and analysis of algorithmic thinking difficulties encountering in older ages under the range of learning difficulties in mathematics and dyscalculia.

The evaluation of basic arithmetic algorithms has been until recently the core of mathematical tests in elementary and secondary education. However, it is necessary that students are able to understand, analyze and improve more complex algorithms in order to support further the study of mathematics and science. In this paper, a number of issues concerning algorithmic thinking are explored. In particular, a case study is proposed in order to compare the efficiency of the traditional algorithmic problem solving in relation to problem solving using interactive virtual environment. The findings suggest that when problem solving using interactive interface is used under conditions the results are more efficient comparing to the traditional way of algorithmic problem solving.

The primary research question is the identification of common characteristics and symptoms presented by people with dyscalculia and difficulties in applying algorithmic thinking. Algorithmic thinking is called the ability of comprehension, implementation, evaluation and creation of algorithms. A key point of the search are the features that could be observed in people with problems in algorithmic thinking as well as the analysis and evaluation of difficulties related to mathematical and algorithmic thinking.

This paper is related to the branch of science that deals with artificial neural networks. It is initiated with a view to answer questions about what neural networks is ,to make correlation with the brain and identify some issues that could be arranged with their help. After a short introduction to cognitive science some differences and similarities comparing computer and human brain are mentioned. While the structure of an artificial neuron was described, a comparison to biological neural networks was made and an artificial neuron model description was also provided. Finally some of the most important applications of neural networks were also mentioned.

This thesis deals with the branch of science about neural networks. Computer and human brain differences and similarities and a brief historical overview are mentioned. Artificial neurons were compared with biological neural networks and some of the applications of neural networks are briefly mentioned. Also a description of an artificial neuron model and the most common functions used for activation were given. Some examples of network architecture are also given. Afterwards there is a reference in accordance to the algorithms which are used to train networks as well as some examples of learning. Moreover we analyze some well-known network models such as Perceptron, Back Propagation and Kohonen networks and Hopfield. Those models are classified according to their training process. We present the learning efficiency as a neural networks‟ training. Due this method network parameters are prescribed in order to improve network performance. We set the conditions in which the minimum point appears. The Taylor series were presented as a tool of analyze the surface performance as well as the conditions to achieve which the highest level. More specifically, some algorithms were developed to find this level. We present some significant optimization algorithms such as Steepest Descent, Newton's Method, Conjugate Gradient which were also used to train neural networks. The Conjugate Gradient algorithm is shown in the case of the quadric equation as well as in the optimization using numerical techniques. We were also mentioned training algorithms of Matlab. These algorithms were using the gradient of performance function to determine the change of weights in order minimize this function. We used a specific technique called Back Propagation in order to determine this gradient. The basic algorithm in which the weights are moving in a direction of negative gradient was analyzed first and afterwards more complex algorithms which increased the speed of function‟s convergence.

This work deals with the branch of science that deals with neural networks. It begins providing answers to questions about what neural networks do, their association with the brain and to identify some of the problems which can be solved with their help. After a brief introduction to cognitive science reported the differences and similarities between computer and human brain. On the occasion to describe the structure of an artificial neuron was compared with biological neural networks and provided the description of the model of artificial neuron. At the end of the work there is a short reference to the evolution of neural networks and some of its applications

This work is about the specific disorder of managing mathematical concepts which is known as dyscalculia. Specifically, a correlation between the terms of dyslexia and dyscalculia is given. Furthermore there is a reference about the link between learning difficulties in language and those relating to mathematical logic. The definition of dyscalculia is also given. An important issue of learning difficulties in mathematics is assessment and diagnosis. While evaluating dyscalculia some symptoms of people that display this specificity are mentioned. Finally factors that are likely responsible for this special disorder are analyzed.

Although learning disabilities study is in advanced stage, research related in algorithmic procedures is limited, since interest in this field is recently displayed. This discourse describes some cognitive and algorithmic principles, which rule the development of a software related to dyscalculia. More specifically,this software design is based on principles related to the diagnostic evaluation of dyscalculia . The proposed software is based on the assumption that dyscalculia is noticed due to a lack of understanding of mathematical concepts and aims at understanding the cerebral reproductions of algorithmic procedures. One of the challenges is the analysis and evaluation the difficulties related to algorithmic mathematical thinking. A key point is maping the features of people with problems in algorithmic thinking.

This paper discusses the industry of science that deals with neural networks. He
started to give answers to questions about what are neural networks to make their
relationship with the brain and to identify some of the problems can be solved with
their help. After a brief introduction to cognitive science reported the differences and
similarities between computer and human brain. Prompted by the description of the
structure of an artificial neuron compared with biological neural networks and
provided the description of the model of artificial neuron. At the end of the work
mentioned briefly some of the most important applications of neural networks.