We Must Invest in Applied Knowledge of Computational Neurosciences and Neuroinformatics as an Important Future in Malaysia: The Malaysian Brain Mapping Project

The Academy of Sciences Malaysia and the Malaysian Industry-Government group for High Technology has been working hard to project the future of big data and neurotechnology usage up to the year 2050. On the 19 September 2016, the International Brain Initiative was announced by US Under Secretary of State Thomas Shannon at a meeting that accompanied the United Nations’ General Assembly in New York City. This initiative was seen as an important effort but deemed costly for developing countries. At a concurrent meeting hosted by the US National Science Foundation at Rockefeller University, numerous countries discussed this massive project, which would require genuine collaboration between investigators in the realms of neuroethics. Malaysia’s readiness to embark on using big data in the field of brain, mind and neurosciences is to prepare for the 4th Industrial Revolution which is an important investment for the country’s future. The development of new strategies has also been encouraged by the involvement of the Society of Brain Mapping and Therapeutics, USA and the International Neuroinformatics Coordinating Facility.

Algoritmos de aprendizaje automático para la clasificación de neuronas piramidales afectadas por el envejecimiento / Machine learning algorithms for classification of pyramidal neurons affected by aging

Una caracterización morfológica precisa de las múltiples clases neuronales del cerebro facilitaría la elucidación de la función cerebral y los cambios funcionales que subyacen a los trastornos neurológicos tales como enfermedades de Parkinson o la Esquizofrenia. El análisis morfológico manual es muy lento y sufre de falta de exactitud porque algunas características de las células no se cuantifican fácilmente. Este artículo presenta una investigación en la clasificación automática de un conjunto de neuronas piramidales de monos jóvenes y adultos, las cuales degradan su estructura morfológica con el envejecimiento. Un conjunto de 21 características se utilizaron para describir su morfología con el fin de identificar las diferencias entre las neuronas. En este trabajo se evalúa el desempeño de cuatro métodos de aprendizaje automático populares en la clasificación de árboles neuronales. Los métodos de aprendizaje de máquinas utilizadas son: máquinas de vectores soporte (SVM), k-vecinos más cercanos (KNN), regresión logística multinomial (MLR) y la red neuronal de propagación hacia atrás (BPNN). Los resultados mostraron las ventajas de MLR y BPNN con respecto a los demás para estos fines. Estos algoritmos de clasificación automáticaofrecen ventajas sobre la clasificación manualbasada en expertos.Mientras que la neurociencia está pasando rápidamente a datos digitales, los principios detrás de los algoritmos de clasificación automática permanecen a menudo inaccesibles para los neurocientíficos, lo que limita las posibilidades de avances(AU)

Accurate morphological characterization of the multiple neuronal classes of the brain would facilitate the elucidation of brain function and the functional changes that underlie neurological disorders such as Parkinson's diseases or Schizophrenia. Manual morphological analysis is very time-consuming and suffers from a lack of accuracy because some cell characteristics are not readily quantified. This paper presents an investigation in the automatic classification of a data set of pyramidal neurons of young and adult monkeys, which degrade his morphologic structure with the aging. A set of 21 features were used to describe their morphology in order to identify differences between neurons. Thispaper evaluates the performance of four popular machine learning methods, in the classification of neural trees. The machine learning methods used are: support vector machines (SVMs), k-nearest neighbors (KNN), multinomial logistic regression (MLR) and back propagation neural network (BPNN). The results showed the advantages of MLR and BPNN with respect to others for this purposes. These automatic classification algorithms offer advantages over manual expert based classification. While neuroscience is rapidly transitioning to digital data, the principles behind automatic classification algorithms remain often inaccessible to neuroscientists, limiting the potential for breakthroughs(AU)

Another Important News from the Neuronman: Malaysia’s Neuroscience Group Moves upwards in Terms of Research, Creativity, and Innovation

12 months ago the first Neuroscience special issue of the Malaysia Journal of Medical Sciences was born with the intention to increase the number of local publication dedicated to neurosciences. Since then many events happened in the neuroscience world of Malaysia, those considered major were the establishment of a Neurotechnology Foresight 2050 task force by the Academy of Medicine Malaysia as well as the launching of Malaysia as the 18th member to join the International Neuroinformatics Coordinating Facility on the 9th October 2015 which was officiated by the Deputy Ministers of Higher Education, Datuk Mary Yap.

Neuroscience and Brain Science Special Issue begins in the Malaysian Journal of Medical Sciences

The Malaysian Journal of Medical Sciences and the Orient Neuron Nexus have amalgated to publish a yearly special issue based on neuro- and brain sciences. This will hopefully improve the quality of peer-reviewed manuscripts in the field of fundamental, applied, and clinical neuroscience and brain science from Asian countries. One focus of the Universiti Sains Malaysia is to strengthen neuroscience and brain science, especially in the field of neuroinformatics.

Learning about brain physiology and complexity from the study of the epilepsies

The brain is a complex system, which produces emergent properties such as those associated with activity-dependent plasticity in processes of learning and memory. Therefore, understanding the integrated structures and functions of the brain is well beyond the scope of either superficial or extremely reductionistic approaches. Although a combination of zoom-in and zoom-out strategies is desirable when the brain is studied, constructing the appropriate interfaces to connect all levels of analysis is one of the most difficult challenges of contemporary neuroscience. Is it possible to build appropriate models of brain function and dysfunctions with computational tools? Among the best-known brain dysfunctions, epilepsies are neurological syndromes that reach a variety of networks, from widespread anatomical brain circuits to local molecular environments. One logical question would be: are those complex brain networks always producing maladaptive emergent properties compatible with epileptogenic substrates? The present review will deal with this question and will try to answer it by illustrating several points from the literature and from our laboratory data, with examples at the behavioral, electrophysiological, cellular and molecular levels. We conclude that, because the brain is a complex system compatible with the production of emergent properties, including plasticity, its functions should be approached using an integrated view. Concepts such as brain networks, graphics theory, neuroinformatics, and e-neuroscience are discussed as new transdisciplinary approaches dealing with the continuous growth of information about brain physiology and its dysfunctions. The epilepsies are discussed as neurobiological models of complex systems displaying maladaptive plasticity.