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1、神经信息学NeuroinformaticsNeuroinformaticsSpring semester, 2009LECTURE 1 Introduction武志华 中科院生物物理所，脑与认知科学国家重点实验室，副研究员 Tel: 64869355Email: 史忠植 中科院计算技术研究所, 智能科学实验室，研究员 人工智能 （神经计算）教学按排每周四: 8:50-11:4040 学时, 2 学分 3学时/每次 地点：玉泉路园区 305 闭卷笔试笔试内容与作业的关系引言 1994年,汪云九老师在中科院研究生院开设“神经信息学”课程。 What is Neuroinformatics? Wha
2、t is I. Neuroinformatics?II. Computational Neuroscience (计算神经科学)?III. Theoretical Neuroscience?IV. Neurocomputing (神经计算)? V.Why we learn “Neuroinformatics” or “Computational Neuroscience” ?VI. Course structure1. databases of neuroscience data, 2. tools for management, sharing, analyzing and modeling
3、 of neuroscience data at all levels of analysis, 3. computational models of the nervous system and neural processesNeuroinformaticsNeuroinformatics is a research field including the development of:Neuro- scienceInformation scienceNeuro- informaticsVast amounts of diverse data about the Vast amounts
4、of diverse data about the brain was gatheredbrain was gathered( (汪云九老师汪云九老师) )Human Brain Project1993年“人类脑计划（Human Brain Project）”的第一批项目公 布，标志着人类脑计划正式启动 Idea emerged in 1991: Mapping the brain and its functions. Integrating enabling technologies into neuroscience researchNeuroinformatics uses databa
5、ses, the Internet, and visualization in the storage and analysis of the neuroscience data(A name similar to Human Genome Project) SenseLab (http:/senselab.med.yale.edu)1 31 32 2BrainMaps.org (http:/ brainmaps.org) Explore BrainMaps data in 3DNeuroinformatics = Databases + Tools + Computational Model
6、sWhat is I. Neuroinformatics?II. Neurocomputing (神经计算)? III. Theoretical Neuroscience?IV. Computational Neuroscience (计算神经科学)?V.Why we learn “Neuroinformatics” or “Computational Neuroscience” ?VI. Course structureNeurocomputing is concerned with processing information:1. It involves a learning proce
7、ss within an artificial neural network architecture 2. The trained networks can be used to perform certain tasks depending on the particular application 3. Neurocomputing can play an important role in solving certain difficult problems in science and engineering such as pattern recognition, optimiza
8、tion, event classification, control and identification of nonlinear systems, and statistical analysisI. What is Neuroinformatics?II. Neurocomputing (神经计算)? III. Theoretical Neuroscience?IV. Computational Neuroscience (计算神经科学)?V.Why we learn “Neuroinformatics” or “Computational Neuroscience” ? VI. Co
9、urse structureTheoretical Neuroscience =Computational and Mathematical Modeling of Neural Systems =Computational NeuroscienceWhat is I. Neuroinformatics?II. Neurocomputing (神经计算)? III. Theoretical Neuroscience?IV. Computational Neuroscience (计算神经科学)?V.Why we learn “Neuroinformatics” or “Computationa
10、l Neuroscience” ? VI. Course structureComputational neuroscience is a subfield of neuroscience that uses mathematical methods to simulate and understand the function of the nervous system (http:/www.scholarpedia.org)Neuro- scienceArtifical Neural networksDynamical systemComputational neuroscience Co
11、mputational neuroscienceA family member of brain science. Computer simulations of neurons and neural networks are complementary to traditional techniques in neuroscienceTheoretical analysis and computational modeling are important tools for characterizing what nervous systems do, determining how the
12、y function, and understanding why they operate in particular waysNeuroscience encompasses approaches ranging from molecular and cellular studies to human psychophysics and psychologyTheoretical neuroscience encourages cross-talk among these subdisciplines by constructing compact representations of w
13、hat has been learned, building bridges between different levels of description, and identifying unifying concepts and principlesFirst neuron model McCulloch & Pitts model (1943)(Bulletin of Mathematical Biophysics 5:115-133)Range: (0, 1) or (-1, 1)Time: t t+1MP model simulates a few properties The u
14、nit has two states depending on the threshold: rest or activated Two types of synapses: inhibitory and excitatory The unit receives the linear sum of all the pre-synaptic inputs The introduction of time, mimicking the synaptic delayAdvantage: Be able to perform logic operationsShortcoming: Too simpl
15、e to model the real neuronGoal1. The first goal is to teach WHY mathematical and computational methods are important in understanding the structure, function and dynamics of neural organization2. The second goal is to explain HOW neural phenomena occurring at different hierarchical levels can be des
16、cribed by proper mathematical modelsWhat is I. Neuroinformatics?II. Neurocomputing (神经计算)? III. Theoretical Neuroscience?IV. Computational Neuroscience (计算神经科学)?V.Why we learn “Neuroinformatics” or “Computational Neuroscience” ? VI. VI. Course structureWhy not go out for a walk? I mean the current neuroscience world is a little different fr
