Why Build a Virtual Brain? Large-Scale Neural Simulations as Jump Start for Cognitive Computing.

Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study elucidates this claim and argues that the main challenge this era is facing is not the lack of biological realism. The challenge lies in identifying general neurocomputational principles for the design of artificial systems, which could display the robust flexibility characteristic of biological intelligence

Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation

Technological integration of Artificial Intelligence (AI) and machine learning in the Prosthetic and Orthotic industry and in the field of assistive technology has become boon for the Persons with Disabilities. The concept of neural network has been used by the leading manufacturers of rehabilitation aids for simulating various anatomical and biomechanical functions of the lost parts of the human body. The involvement of human interaction with various agents’ i.e. electronic circuitry, software, robotics, etc. has made a revolutionary impact in the rehabilitation field to develop devices like Bionic leg, mind or thought control prosthesis and exoskeletons. Application of Artificial Intelligence and robotics technology has a huge impact in achieving independent mobility and enhances the quality of life in Persons with Disabilities (PwDs)

Machine learning for financial applications: self-organising maps, hierarchical clustering and dynamic time-warping for portfolio constructive

This study investigates how modern machine learning (ML) techniques can be used to advance the field of quantitative investing. A broad literature review evaluated the common applications for ML in finance, and what ML algorithms are being used. The results show ML is commonly applied to the areas of Return Forecasting, Portfolio Construction, Ethics, Fraud Detection Decision Making Language Processing and Sentiment Analysis. Neural Network technology and support vector machine are identified as popular ML algorithms. A second review was carried out, focusing in the area of ML for quantitative finance in recent years finds three primary areas; Return forecasting, Portfolio construction and Risk management. A practical ML experiment carried out as a proof of concept of ML for financial applications. This experiment was informed by the results of the broad and more focused literature searches. Two forms of ML techniques are used to analyse market return data and equity flow data (provided by State Street Global Markets) and create a portfolio from insights derived from the ML technology. The ML technologies employed are those of Self-Organising Maps and Hierarchical Clustering. The portfolios created were tested in terms of risk, profitability and stability. Stable regimes and profitable portfolios are created. Results show that portfolios obtained by analysing equity flow data consistently outperform those created by analysing return data

Simuladores cerebrales: revisión de modelos micro- y macroescala

Las simulaciones de redes cerebrales pretenden comprender las funciones del cerebro tanto en condiciones normales como patológicas. A tal fin, existen en la actualidad múltiples simuladores y paquetes software pertenecientes al ámbito de la neurociencia computacional. El objetivo final de los modelos computacionales consiste en tratar de explicar la relación entre la estructura, la función y la dinámica cerebrales. Los modelos multiescala trabajan con datos biológicos de distinto tipo y granularidad, en un rango que va desde modelos de neuronas, sinapsis y microcircuitos -microescala- hasta modelos a macroescala con cerebros virtuales. En el presente trabajo se pretende realizar una revisión de los modelos microescala y macroescala, resaltando sus principales características y funcionalidades y su orientación para investigación. Finalmente, se trabajará experimentalmente con un modelo macroescala, para el cual se elaborará una guía de usuario.Grado en Ingeniería Biomédic

次世代スーパーコンピュータ環境における効率的かつ大規模な詳細神経回路シミュレーション手法に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 神崎 亮平, 東京大学特任教授 藤谷 秀章, 東京大学特任講師 安藤 規泰, 兵庫県立大学教授 池野 英利, 理化学研究所センター長 姫野 龍太郎University of Tokyo(東京大学