神经信息学NeuroinformaticsNeuroinformatics

Spring semester, 2009

LECTURE 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: 64869355

Email: wuzh@moon.ibp.ac.cn

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 : 8:50-11:40

40 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?

What 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 structure

1. databases of neuroscience data,

2. tools for management, sharing, analyzing and

modeling of neuroscience data at all levels of analysis,

3. computational models of the nervous system and neural processes

NeuroinformaticsNeuroinformatics is a research field including the development of:

Neuro-science

Information science

Neuro-informatics

Vast amounts of diverse data about the Vast amounts of diverse data about the brain was gatheredbrain was gathered

(( 汪云九老汪云九老师师 ))

Human Brain Project

1993 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 research

Neuroinformatics uses databases, 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 3

22

BrainMaps.org (http:// brainmaps.org)

Explore BrainMaps data in 3D

Neuroinformatics =

Databases + Tools + Computational Models

What 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 structure

Neurocomputing is concerned with processing information:

1. It involves a learning process within an artificial neural network architecture2. The trained networks can be used to perform certain tasks

depending on the particular application3. Neurocomputing can play an important role in solving

certain difficult problems in science and engineering such as pattern recognition, optimization, event classification, control and identification of nonlinear systems, and statistical analysis

I. 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. Course structure

Theoretical Neuroscience =

Computational and Mathematical Modeling of Neural Systems =

Computational Neuroscience

What 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 structure

Computational 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-science

ArtificalNeural

networks

Dynamicalsystem

Computationalneuroscience

Computational neuroscience

• A family member of brain science. Computer simulations of neurons and neural networks are complementary to traditional techniques in neuroscience

• Theoretical analysis and computational modeling are important tools for characterizing what nervous systems do, determining how they function, and understanding why they operate in particular ways

• Neuroscience encompasses approaches ranging from molecular and cellular studies to human psychophysics and psychology

Theoretical neuroscience encourages cross-talk among these subdisciplines by constructing compact representations of what has been learned, building bridges between different levels of description, and identifying unifying concepts and principles

First neuron model － McCulloch & Pitts model (1943)

(Bulletin of Mathematical Biophysics 5:115-133)

Range: (0, 1) or (-1, 1)

Time: t t+1

MP model simulates a few properties

• The unit 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 delay

Advantage: Be able to perform logic operations Shortcoming: Too simple to model the real neuron

Goal

1. The first goal is to teach WHY mathematical and computational methods are important in understanding

the structure, function and dynamics of neural organization

2. The second goal is to explain HOW neural phenomena occurring at different hierarchical levels can be described by proper mathematical models

What 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 structure

Why not go out for a walk? I mean the current neuroscience world is a little different from before

For example:The Age of Brain Science （ Japan ）

• Understanding the Brain• Protecting the Brain• Creating the Brain• Nurturing the Brain（ October 1997--- March 2008 ）

If you are a student majoring in experimental biology, － you may figure out your experimental problem or analyze results in a different way

If you are a student having good mathematical or physical background, － ??? in future

About mathematical and physical FORMULA:

－ High school knowledge is enough

Caveat

Computational neuroscience is a huge and fast developing area

This is only a very short course, and hopefully it can provide:

－ The basic concepts and methods of computational neuroscience research

－ Some brief introduction to neurobiological concepts and mathematical techniques. The techniques will be applied for describing the behavior of several brain regions

What 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 structure

Course Structure

0. Introduction: What is computational neuroscience, why is it urgently needed

1. Single neuron models

2. Neural network models

3. Neural Coding

4. Synaptic plasticity and learning

5. Hot points in brain modeling

6. Neurocomputing (by 史忠植老师 )

Recommended Readings

1. Thomas P. Trappenberg: Fundamentals of Computational Neuroscience. Oxford University Press, 2002

2. Peter Dayan & Larry F. Abbott: Theoretical Neuroscience. MIT Press, Cambridge. 2001

3. Koch C & Segev I: Methods in neuronal modeling: from ions to networks. MIT Press, Cambridge, 1998

4. 汪云九等著，神经信息学 . 北京 :高等教育出版社 . 2006

5. 郭爱克著 , 计算神经科学 . 上海科技教育出版社 . 2000

6. 史忠植，智能科学 . 清华大学出版社， 2006

7. Jeff Hawkins and Sandra Blakeslee, On Intelligence. Times Books 2004 人工智能的未来 . 贺俊杰等译 , 陕西科学技术出版

社 , 2006

8. F. Crick （汪云九等译）， < 惊人的假说 > ，湖南科学技术出版社，长沙， 1998

作业及思考题

1 神经信息学主要包括哪些内容？

2 什么是计算神经科学？计算神经科学与神经计算的区别？

3 第一个形式人工神经元模型是什么？模拟了神经元的哪 几个性质？