| 山田 達也 M1回目 | 論理生命学 |
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発表題目：Identification of a molecular system which regulates growth cone membrane potential during growth cone guidance
発表概要：Correct wiring of neural network is a fundamental developmental process for brain functions. In the wiring, a neural fiber is guided by the growth cone located at a tip of the fiber (growth cone guidance). The growth cone detects extracellular information (concentration gradients of guidance molecules) and decides a direction to which the neural fiber elongates. However, the signal transduction among molecules is largely unknown. In this presentation, I will report our approach overview and the interim results of identification of the molecular system related to the growth cone guidance by using the experimental data of membrane potential. 神経系を有する動物の発達過程では，機能獲得のために神経細胞間の適切な配線が必要である．配線は神経突起先端にある成長円錐によって行われる（成長円錐誘導）．このとき，成長円錐は細胞外の情報（誘導分子の濃度勾配等）から突起の伸長方向を決定する．しかしながら，そのための分子に関する情報伝達の仕組みは未解明である．本発表では，実験計測された膜電位変化から，成長円錐誘導に関わる細胞内分子システムの同定を試みた結果を報告する． | |
| Sukhanov Paul M1回目 | 論理生命学（計算神経科学） |
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発表題目： Neural Decoding of Visually Tracked 3-D Objects 発表概要： Neural Decoding is a recently developed paradigm in neuroscience that relies on the techniques of machine learning to infer information about a subjects perceptual experiences through analysis of neuro-imaging data such as fMRI. For example, accurate prediction of which of several categories of objects a subject is looking at (Haxby, 2001) or more recently, the reconstruction of arbitrary visually presented 10x10 pixel images (Miyawaki, 2008) from fMRI signals has been achieved. To extend the above visual image reconstruction to more complicated, real-life perceptual events would require accurate decoding of moving, 3-dimensional objects. The feasibility of applying decoding and/or reconstruction methods to moving, 3-dimensional stimuli will be investigated in the proposed research. | |
| To Thi Chinh M1回目 | 自然言語処理学 |
| 1051206 To Thi Chinh M1 Natural Language Processing Lab Presentation Topic (発表題目)： Division of complex sentences and Acquisition of syntactic pattern for Statistical Machine Translation Abstract（発表概要）: My research topic is about English-Japanese Statistical Machine Translation. Particularly,statistical machine translation approach has difficulties in learning the model of languages of different syntactic structures. The presentation introduces my interested topic on dividing complex and compound sentences into simpler clauses for machine translation. Also,I would like to work on acquiring the patterns of those sentences, build a parallel model for translating complex and compound sentences in languages of different syntactic structures, | |