The Neuronal Dissociation of Rule-Based versus Similarity-Based Learning
neural discrete representation learning
neural discrete representation
learning
Neural discrete representation learning，也叫神经离散表示学习，是深度学习领域一个重要的任务，它旨
在从原始信号中抽取出离散表示，以便进行有效分析。
它
也被称为“抽象学习”，因为它不仅仅涉及识别和分类，
而且还能够将复杂物体抽象成离散表示，从而帮助人们对
复杂的现实世界建立模型。
神经离散表示学习的目的是从原始信号中抽取出离散
表示，以便进行有效分析。
这些离散表示可以通过神经网
络的监督或者无监督的方式学习得到。
这种学习方法在结
构化学习、自然语言处理以及计算机视觉等领域都有广泛
的应用。
神经离散表示学习的最终目标是从原始信号中抽取出
更小、更具概括性的表示，以便更有效地分析和使用。
神
经离散表示学习的结果可以用于多种应用，如目标识别、
语义分析、文本表示学习等。
神经离散表示学习的方法主要有三种：
1、监督学习：这种学习方法需要有标签的原始数据，通过对标签数据的学习，来获得离散表示；
2、无监督学习：这种学习方法不需要标签的原始数据，而是利用原始数据的内在联系或者聚类的方式来学习离散表示；
3、半监督学习：这种学习方法是一种折衷的方法，它结合了监督学习和无监督学习的优点，使用结合标签和未标签数据的方式来学习离散表示。
神经离散表示学习有助于实现机器智能的感知功能，可以帮助计算机将复杂的现实世界的信息抽象成离散表示，从而更好地理解和分析信息。
同时，它也能够帮助机器学习自动生成有意义的离散表示，从而改善模型的准确性和泛化能力。
再励学习与神经控制
j
上式可由线性神经网络实现，网络学习算法：
e ( k ) x j ( k ) v j ( k ) 3 re ( k ) r
6
2. 评价预测学习规则
再励学习用于神经控制的基本思想： 不需已知对象模型，且没有足够知识的情况下，通过学习机制 对环境的交互，评价控制的优劣，用‘奖或惩’算法训练控制器， 使之对复杂的非线性、不确定、不确知系统，达到有效的控制。 阐述基于评价预测的再励学习神经控制。
8
1. TD法
u( k ) 为控制器输出，对象状态方程： 设x( k ) 为被控对象状态； x( k 1) f [x( k ), u( k )] 再励信号re ( k ) 是状态与输出的函数： re ( k ) r[x( k ), u( k )] 设状态的评价函数V [x( k )] 是由 k 时刻起的再励信号的加权和： V [ x ( k )] re ( k ) re ( k 1) 2 re ( k 2 ) ，0 1 。 由上式有 V [ x( k 1)] re ( k 1) re ( k 2 ) 2 re ( k 3)
原则是，使受到奖励的可能性增大。 可见：再励信号是环境对学习机学习结果的一个评价。
3
如：移动机器人 设机器人当前位置为环境的状态，学习是为求解动作序列，使其以最短路径，从任一位置 到达目标位置，则再励信号可设为
1 re 0
，机器人到目标位置 ，不在目标位置
如：在移动小车上的倒立摆（倒立摆系统），见图 4-9-2 设其当前的位置为环境的状态，学习是为求解作用于小车上的力的序列 F(k)，使倒立摆与 垂线的夹角不大于设定角 0 ，则再励信号可设为
神经达尔文主义
神经达尔文主义1972 年诺贝尔生理医学奖的获得者埃德尔曼（Gerald M.Edelman）在意识问题的研究上做了最实质性的工作，提出了一个运用神经元群体来解释大脑工作的整体（global）理论——神经达尔文主义，或称为神经元群体选择理论。
在描述大脑如何处理外界信息和新信号的方面存在有两个理论，一个理论认为大脑如同计算机或图灵机，另一理论则是基于群体思想的理论。
埃德尔曼倾向于后者，因为他认为群体思想在决定大脑是如何处理个体大脑的众多变异的方面上是很重要的。
这种变异真的存在于所有的结构和功能层次中。
由于所处的环境不同，不同的个体有不同的遗传、不同的后天秩序（epigenetic sequences）、不同的肢体反应和不断变化的环境中的不同经验，结果导致在神经元化学物质、网络结构、突触强度、记忆和价值系统所控制的激励模式等方面都有巨大变异，最终使人与人之间在“意识流”的内容和类型上有着明显的不同。
杰出的神经科学家Karl Lashiley 在评论个体神经系统的可变性时，就承认自己还没有做好充分的准备来解释如此多的变异的存在。
即使大脑展示了很多一般模式而没有显示这些变异，它们也不能仅仅当作噪音处理掉，因为这些变异太多，而且存在于很多组织层次诸如分子、细胞和回路（circuits）等中。
进化完全不可能像一道计算机程序处理噪音一样设计出多重纠错代码来保护大脑中阻止变异产生的模式。
面对神经系统多变性的另一种基本方式是认为变异是普遍存在的。
假设每个个体大脑的局部不同构成了变异群体。
在这种情况下，假如某些价值约束或某些适宜性的限制得以满足，那么即使在不可预测的情况下，这样一个变异群体的选择同样会产生相应的模式，这就是所谓的“适者生存”。
“神经元群体选择理论”理论有三大信条：（1）发育选择(Developmental selection) ：“在神经元解剖结构建立的早期，发育的神经元之间的联结模式中发生后天变异，这些变异在每个由无数变异的回路或神经元群体组成的脑区中产生“节目单”（repertoires）。
学习与记忆(神经生物学)
记忆分类
长时记忆
记忆保持的时间
短时记忆 陈述性记忆 信息储存和回忆的方式 非陈述性记忆
记忆的储存有阶段性
普遍接受的一种记忆分类就是将记忆分成
短时记忆：数秒到数分钟 长时记忆：相对长期稳定，但随时间的推 移会逐渐减弱
记忆的储存有阶段性
记忆储存的阶段性
记忆储存的阶段 性是从短时记忆 向长时记忆的转 化过程 刚学到的新知识 先在短时工作记 忆中加工，然后 经过一步或若干 步转化为永久性 的长时记忆。 当回忆时，一个 搜寻和提取系统 从储存的记忆中 找到所要的信息
Ca2+ 积累→突触前末梢持续释放神 经递质→突触后电位增强
Copyright 2001 by Allyn & Bacon
非联合性学习
敏感化
习惯化仅仅涉及一个反射 敏感化是一个反射回路的兴 回路中的各个神经元 奋对另一个反射回路的影响
联合性学习（associative learning）：
概念：两个或两个以上事件在时间上很 接近地重复发生，最后在脑内逐渐形成 联系。
PKA/PKC磷酸化并开放L型Ca通道，进一步增加Ca内流。
3.
第2、3种功能依赖于PKA和PKC的协同作用。
补充概念：
强直后增强 (posttetanic potentiation)： 定义：突触前末梢受到一短串强直性
刺激后在突触后神经元上产生的突 触后电位增强，可持续60s。
机制：强直性刺激→突触前神经元内
概
念
陈述记忆是有关时间、地点和人物的知识 ，这种记忆需要一个清醒地回忆的过程。 它的形成依赖于评价，比较和推理等认知 过程。 陈述记忆储存的是有关事件或事实的知识 ，它有时经过一次测试或一次经历即可形 成。我们通常所说的记忆就是指的陈述记 忆。
人工智能习题解答
人工智能第1部分绪论1-1.什么是人工智能？试从学科和能力两方面加以说明。
答：从学科方面定义：人工智能是计算机科学中涉及研究、设计和应用智能机器的一个分支。
它的近期目标在于研究用机器来模拟和执行人脑的某些智力功能，并开发相关理论和技术从能力方面定义：人工智能是智能机器所执行的通常与人类智能有关的智能行为，如判断、推理、证明、识别、感知、理解、通信、设计、思考、规划、学习和问题求解等思维活动。
1-2.在人工智能的发展过程中，有哪些思想和思潮起了重要作用？答：1）数理逻辑和关于计算本质的新思想，提供了形式推理概念与即将发明的计算机之间的联系；2）1956年第一次人工智能研讨会召开，标志着人工智能学科的诞生；3）控制论思想把神经系统的工作原理与信息理论、控制理论、逻辑以及计算联系起来，影响了许多早期人工智能工作者，并成为他们的指导思想；4）计算机的发明与发展；5）专家系统与知识工程；6）机器学习、计算智能、人工神经网络和行为主义研究，推动人工智能研究的近一步发展。
1-3.为什么能够用机器（计算机）模仿人的智能？答：物理符号系统的假设：任何一个系统，如果它能够表现出智能，那么它就必定能执行输入符号、输出符号、存储符号、复制符号、建立符号结构、条件迁移6种功能。
反之，任何系统如果具有这6种功能，那么它就能够表现出智能（人类所具有的智能）。
物理符号系统的假设伴随有3个推论。
推论一：既然人具有智能，那么他（她）就一定是各物理符号系统；推论二：既然计算机是一个物理符号系统，它就一定能够表现出智能；推论三：既然人是一个物理符号系统，计算机也是一个物理符号系统，那么我们就能够用计算机来模拟人的活动。
1-4.人工智能的主要研究内容和应用领域是什么？其中，哪些是新的研究热点？答：研究和应用领域：问题求解（下棋程序），逻辑推理与定理证明（四色定理证明），自然语言理解，自动程序设计，专家系统，机器学习，神经网络，机器人学（星际探索机器人），模式识别（手写识别，汽车牌照识别，指纹识别），机器视觉（机器装配，卫星图像处理），智能控制，智能检索，智能调度与指挥（汽车运输高度，列车编组指挥），系统与语言工具。
【计算机科学】_神经网络学习算法_期刊发文热词逐年推荐_20140726
推荐指数 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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科研热词 神经网络 高阶交叉累量 集成神经网络 遗传算法 迁移学习 调制识剐 自适应 聚类分析 结构自适应确定 级联模糊神经网络 离子群优化算法 泛函网络 模糊模型 模糊推理 概念格 权值直接确定 数字学习 故障诊断 循环结构 形式背景 形式概念分析 序列图像 层次贝叶斯 学习算法 多目标识别 多学位识别 回归 双向联想记忆 压缩映射原理 rbf神经网络 hu矩 chebyshev正交基 bp神经网络
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2011年 科研热词 预测误差 神经网络 短期负荷预测 模糊神经网络 模糊推理 数字识别 改进bp网络 安全态势 天气预测 l-m优化法 bp算法 推荐指数 1 1 1 1 1 1 1 1 1 1 1
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hebbian规则
Hebbian规则是一种用于描述神经元之间突触连接权重改变的经典学习规则。
它是由加拿大心理学家Donald O. Hebb于1949年提出的，被称为"Hebbian learning"（Hebb学习）。
Hebbian规则是基于神经元之间的活动相关性原理，它表达了当一个神经元（A）激活时，与其连接的另一个神经元（B）的突触连接权重应该增强的想法。
具体来说，Hebbian规则可以描述为："If cell A is near enough to excite cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased."简而言之，当一个神经元的活动频繁导致其他神经元的激活，那么这两个神经元之间的连接权重将增强。
Hebbian规则的核心思想是，神经元之间的连接权重调整与它们之间的活动相关性有关。
当两个神经元的活动频繁地同时发生，Hebbian规则认为这两个神经元之间的连接应该增强，从而加强它们之间的关联性。
这种学习规则类似于“细胞之间的联接强化规则”。
尽管Hebbian规则对于解释神经元之间的连接形成和学习过程具有重要意义，但它也有其局限性。
具体来说，Hebbian规则没有考虑到稳定性问题，即在长时间的学习中可能导致权重过度增长，进而影响系统的稳定性。
因此，在实际应用中，通常会结合其他学习规则来更好地调整神经网络的连接权重。
演绎与归纳推理比较的神经机制：问题与趋势
Advances in Psychology 心理学进展, 2016, 6(4), 376-383Published Online April 2016 in Hans. /journal/ap/10.12677/ap.2016.64049The Neural Mechanisms of Comparisonbetween Deductive and InductiveReasoning: Problems and TrendsXiaofang Li, Mingming Zhang, Changquan Long*Faculty of Psychology, Southwest University, ChongqingReceived: Mar. 16th, 2016; accepted: Apr. 1st, 2016; published: Apr. 11th, 2016Copyright © 2016 by authors and Hans Publishers Inc.This work is licensed under the Creative Commons Attribution International License (CC BY)./licenses/by/4.0/AbstractDeductive reasoning and inductive reasoning are two main forms of reasoning. Single-process ac-counts and dual-process accounts are two competing theories of reasoning psychology. At present, many studies compare deductive and inductive reasoning using cognitive neuroscience technolo-gy to test whether reasoning is a single or double process. But there are many problems in the studies: limitations of forward inference, differences in cognitive neuroscience techniques, com-plex and varied experimental tasks, challenges of cognitive neuroscience itself and so on. In future research, forward inference can still be the basic logic and breakthrough of studies; multivariate techniques and standard experimental tasks should be conducted; and studies on the neural me-chanisms of comparison between deductive and inductive reasoning should go deep into more microscopic level, such as the level of molecule and neuron.KeywordsDeductive Reasoning, Inductive Reasoning, Psychological Theories of Reasoning, CognitiveNeuroscience, Problems, Trends演绎与归纳推理比较的神经机制：问题与趋势李晓芳，张明明，龙长权*西南大学心理学部，重庆*通讯作者。
基于局部加权k近邻的多机器人系统异步互增强学习
（Ｃｏｌ ｅｏ ｆｒ ｔ ｎＥ ｇｎｅｉｇ ａｇ ｈｕＵｎｖｒｔ Ｙａｇ ｕ２ ５０ Ｃ ｉａ ｌｇ ｆ ｎｏｍａｏ ｎ ｉｅｒ ，Ｙ ｎｚｏ ｉｅｓｙ， ｎ ￣ｏ ２０ ９， ｈｎ ） ｅ Ｉ ｉ ｎ ｉ （ ｔｔ Ｋ ｙＬ ｂ ｒｔｒ ｆ ｎｇｍｅｔ ｄＣ ｎｒｌ ｏ ｏ ｌ ｙｔ ，Ｉｓｔｔ ｆ ｕｏ ｔ ｎ Ｓａｅ ｅ ａｏａ ｙｏ ａ ｅ ｎ ｏ ｔ ｒＣ ｍｐｅ Ｓ ｓ ｍｓ ｎｔｕｅｏ ｔｍａｉ ， ｏ Ｍａ ｎ ａ ｏｆ ｘ ｅ ｉ Ａ ｏ
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Ｓｐ Ｉ ｕ （）
Ｓ ｐ ．２ ２ ｅ ｔ ０１
ｄ ｉ１ ．９ ９ ｊｉ ｎ １０ — ５ ５ ２ １ ． １０ ２ ｏ ：０ ３ ６ ／．ｓ ．０ １ ０ ０ ．０ ２ Ｓ ．４ ｓ
libin英文翻译NEURAL NETWORKS
┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊英文文章NEURAL NETWORKSby Christos Stergiou and Dimitrios SiganosAbstractThis report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated.1. Introduction to neural networks1.1 What is a Neural Network?An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.1.2 Historical backgroundNeural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras.Many importand advances have been boosted by the use of inexpensive computer emulations. Following an initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period when funding and professional support was minimal, important advances were made by relatively few reserchers. These pioneers were able to develop convincing technology which surpassed the limitations identified by Minsky and Papert. Minsky and Papert, published a book (in 1969) in which they summed up a general feeling of frustration (against neural networks) among researchers, and was thus accepted by most without further analysis. Currently, the neural network field┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊enjoys a resurgence of interest and a corresponding increase in funding. The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits. But the technology available at that time did not allow them to do too much.1.3 Why use neural networks?Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.Other advantages include:1.Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.2.Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.3.Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.4.Fault Tolerance via Redundant Information Coding: Partial destruction ofa network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.1.4 Neural networks versus conventional computersNeural networks take a different approach to problem solving than that of conventional computers. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. But computers would be so much more useful if they could do things that we don't exactly know how to do.Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements(neurones) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. The examples must be selected carefully otherwise useful time is wasted┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊or even worse the network might be functioning incorrectly. The disadvantage is that because the network finds out how to solve the problem by itself, its operation can be unpredictable.On the other hand, conventional computers use a cognitive approach to problem solving; the way the problem is to solved must be known and stated in small unambiguous instructions. These instructions are then converted to a high level language program and then into machine code that the computer can understand. These machines are totally predictable; if anything goes wrong is due to a software or hardware fault.Neural networks and conventional algorithmic computers are not in competition but complement each other. There are tasks are more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks. Even more, a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency. Neural networks do not perform miracles. But if used sensibly they can produce some amazing results.2. Human and Artificial Neurones - investigating the similarities2.1 How the Human Brain Learns?Much is still unknown about how the brain trains itself to process information, so theories abound. In the human brain, a typical neuron collects signals from others through a host of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurones. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊Components of a neuronThe synapse2.2 From Human Neurones to Artificial NeuronesWe conduct these neural networks by first trying to deduce the essential features of neurones and their interconnections. We then typically program a computer to simulate these features. However because our knowledge of neurones is incomplete and our computing power is limited, our models arenecessarily gross idealisations of real networks of neurones.The neuron model3. An engineering approach3.1 A simple neuronAn artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not.┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊A simple neuron3.2 Firing rulesThe firing rule is an important concept in neural networks and accounts for their high flexibility. A firing rule determines how one calculates whether a neuron should fire for any input pattern. It relates to all the input patterns, not only the ones on which the node was trained.A simple firing rule can be implemented by using Hamming distance technique. The rule goes as follows:Take a collection of training patterns for a node, some of which cause it to fire (the 1-taught set of patterns) and others which prevent it from doing so (the 0-taught set). Then the patterns not in the collection cause the node to fire if, on comparison , they have more input elements in common with the 'nearest' pattern in the 1-taught set than with the 'nearest' pattern in the 0-taught set. If there is a tie, then the pattern remains in the undefined state.For example, a 3-input neuron is taught to output 1 when the input (X1,X2 and X3) is 111 or 101 and to output 0 when the input is 000 or 001. Then, beforeAs an example of the way the firing rule is applied, take the pattern 010. It differs from 000 in 1 element, from 001 in 2 elements, from 101 in 3 elements and from 111 in 2 elements. Therefore, the 'nearest' pattern is 000 which belongs in the 0-taught set. Thus the firing rule requires that the neuron should not fire when the input is 001. On the other hand, 011 is equally distant┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊from two taught patterns that have different outputs and thus the output stays undefined (0/1).By applying the firing in every column the following truth table is obtained;The difference between the two truth tables is called the generalisation of the neuron. Therefore the firing rule gives the neuron a sense of similarity and enables it to respond 'sensibly' to patterns not seen during training.3.3 Pattern Recognition - an exampleAn important application of neural networks is pattern recognition. Pattern recognition can be implemented by using a feed-forward (figure 1) neural network that has been trained accordingly. During training, the network is trained to associate outputs with input patterns. When the network is used, it identifies the input pattern and tries to output the associated output pattern. The power of neural networks comes to life when a pattern that has no output associated with it, is given as an input. In this case, the network gives the output that corresponds to a taught input pattern that is least different from the given pattern.Figure 1.For example:The network of figure 1 is trained to recognise the patterns T and H. The associated patterns are all black and all white respectively as shown below.┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊If we represent black squares with 0 and white squares with 1 then the truthTop neuronBottom neuronFrom the tables it can be seen the following associasions can be extracted:In this case, it is obvious that the output should be all blacks since the input pattern is almost the same as the 'T' pattern.┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊Here also, it is obvious that the output should be all whites since theinput pattern is almost the same as the 'H' pattern.Here, the top row is 2 errors away from the a T and 3 from an H. So the top output is black. The middle row is 1 error away from both T and H so the output is random. The bottom row is 1 error away from T and 2 away from H. Therefore the output is black. The total output of the network is still in favour of the T shape.3.4 A more complicated neuronhe previous neuron doesn't do anything that conventional conventional computers don't do already. A more sophisticated neuron (figure 2) is the McCulloch and Pitts model (MCP). The difference from the previous model is that the inputs are 'weighted', the effect that each input has at decision making is dependent on the weight of the particular input. The weight of an input is a number which when multiplied with the input gives the weighted input. These weighted inputs are then added together and if they exceed a pre-set threshold value, the neuron fires. In any other case the neuron does notfire.Figure 2. An MCP neuronIn mathematical terms, the neuron fires if and only if;X1W1 + X2W2 + X3W3 + ... > TThe addition of input weights and of the threshold makes this neuron a very flexible and powerful one. The MCP neuron has the ability to adapt to┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊a particular situation by changing its weights and/or threshold. Various algorithms exist that cause the neuron to 'adapt'; the most used ones are the Delta rule and the back error propagation. The former is used in feed-forward networks and the latter in feedback networks.4 Architecture of neural networks4.1 Feed-forward networksFeed-forward ANNs (figure 1) allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organisation is also referred to as bottom-up or top-down.4.2 Feedback networksFeedback networks (figure 1) can have signals travelling in both directions by introducing loops in the network. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their 'state' is changing continuously until they reach an equilibrium point. They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denotefeedback connections in single-layer organisations.Figure 4.1 An example of a simplefeedforward networkFigure 4.2 An example of a complicatednetwork4.3 Network layersThe commonest type of artificial neural network consists of three groups, or layers, of units: a layer of "input" units is connected to a layer of "hidden" units, which is connected to a layer of "output" units. (see Figure┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊4.1)The activity of the input units represents the raw information that is fed into the network.The activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and the hidden units.The behaviour of the output units depends on the activity of the hidden units and the weights between the hidden and output units.This simple type of network is interesting because the hidden units are free to construct their own representations of the input. The weights between the input and hidden units determine when each hidden unit is active, and so by modifying these weights, a hidden unit can choose what it represents.We also distinguish single-layer and multi-layer architectures. The single-layer organisation, in which all units are connected to one another, constitutes the most general case and is of more potential computational power than hierarchically structured multi-layer organisations. In multi-layer networks, units are often numbered by layer, instead of following a global numbering.4.4 PerceptronsThe most influential work on neural nets in the 60's went under the heading of 'perceptrons' a term coined by Frank Rosenblatt. The perceptron (figure 4.4) turns out to be an MCP model ( neuron with weighted inputs ) with some additional, fixed, pre--processing. Units labelled A1, A2, Aj , Ap are called association units and their task is to extract specific, localised featured from the input images. Perceptrons mimic the basic idea behind the mammalian visual system. They were mainly used in pattern recognition even though theircapabilities extended a lot more.Figure 4.4In 1969 Minsky and Papert wrote a book in which they described the limitations┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊of single layer Perceptrons. The impact that the book had was tremendous and caused a lot of neural network researchers to loose their interest. The book was very well written and showed mathematically that single layer perceptrons could not do some basic pattern recognition operations like determining the parity of a shape or determining whether a shape is connected or not. What they did not realised, until the 80's, is that given the appropriate training, multilevel perceptrons can do these operations.5. The Learning ProcessThe memorisation of patterns and the subsequent response of the network can be categorised into two general paradigms: associative mapping in which the network learns to produce a particular pattern on the set of input units whenever another particular pattern is applied on the set of input units. The associtive mapping can generally be broken down into two mechanisms: auto-association: an input pattern is associated with itself and the states of input and output units coincide. This is used to provide pattern completition, ie to produce a pattern whenever a portion of it or a distorted pattern is presented. In the second case, the network actually stores pairs of patterns building an association between two sets of patterns.hetero-association: is related to two recall mechanisms:nearest-neighbour recall, where the output pattern produced corresponds to the input pattern stored, which is closest to the pattern presented, and interpolative recall, where the output pattern is a similarity dependent interpolation of the patterns stored corresponding to the pattern presented. Yet another paradigm, which is a variant associative mapping is classification, ie when there is a fixed set of categories into which the input patterns are to be classified.regularity detection in which units learn to respond to particular properties of the input patterns. Whereas in asssociative mapping the network stores the relationships among patterns, in regularity detection the response of each unit has a particular 'meaning'. This type of learning mechanism is essential for feature discovery and knowledge representation.Every neural network posseses knowledge which is contained in the values of the connections weights. Modifying the knowledge stored in the network as a function of experience implies a learning rule for changing the values of the weights.┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊Information is stored in the weight matrix W of a neural network. Learning is the determination of the weights. Following the way learning is performed, we can distinguish two major categories of neural networks: fixed networks in which the weights cannot be changed, ie dW/dt=0. In such networks, the weights are fixed a priori according to the problem to solve.adaptive networks which are able to change their weights, ie dW/dt not= 0.All learning methods used for adaptive neural networks can be classified into two major categories:Supervised learning which incorporates an external teacher, so that each output unit is told what its desired response to input signals ought to be. During the learning process global information may be required. Paradigms of supervised learning include error-correction learning, reinforcement learning and stochastic learning.An important issue conserning supervised learning is the problem of error convergence, ie the minimisation of error between the desired and computed unit values. The aim is to determine a set of weights which minimises the error. One well-known method, which is common to many learning paradigms is the least mean square (LMS) convergence.Unsupervised learning uses no external teacher and is based upon only local information. It is also referred to as self-organisation, in the sense that it self-organises data presented to the network and detects their emergent collective properties. Paradigms of unsupervised learning are Hebbian lerning and competitive learning.Ano2.2 From Human Neurones to Artificial Neuronesther aspect of learning concerns the distinction or not of a seperate phase, during which the network is trained, and a subsequent operation phase. We say that a neural network learns off-line if the learning phase and the operation phase are distinct.A neural network learns on-line if it learns and operates at the same time.┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊Usually, supervised learning is performed off-line, whereas usupervised learning is performed on-line.5.1 Transfer FunctionThe behaviour of an ANN (Artificial Neural Network) depends on both the weights and the input-output function (transfer function) that is specified for the units. This function typically falls into one of three categories: linear (or ramp)thresholdsigmoidFor linear units, the output activity is proportional to the total weighted output.For threshold units, the output is set at one of two levels, depending on whether the total input is greater than or less than some threshold value. For sigmoid units, the output varies continuously but not linearly as the input changes. Sigmoid units bear a greater resemblance to real neurones than do linear or threshold units, but all three must be considered rough approximations.To make a neural network that performs some specific task, we must choose how the units are connected to one another (see figure 4.1), and we must set the weights on the connections appropriately. The connections determine whether it is possible for one unit to influence another. The weights specify the strength of the influence.We can teach a three-layer network to perform a particular task by using the following procedure:We present the network with training examples, which consist of a pattern of activities for the input units together with the desired pattern of activities for the output units.We determine how closely the actual output of the network matches the desired output.We change the weight of each connection so that the network produces a better approximation of the desired output.5.2 An Example to illustrate the above teaching procedure:Assume that we want a network to recognise hand-written digits. We might use an array of, say, 256 sensors, each recording the presence or absence of ink in a small area of a single digit. The network would therefore need 256 input units (one for each sensor), 10 output units (one for each kind of digit) and a number of hidden units.┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊For each kind of digit recorded by the sensors, the network should produce high activity in the appropriate output unit and low activity in the other output units.To train the network, we present an image of a digit and compare the actual activity of the 10 output units with the desired activity. We then calculate the error, which is defined as the square of the difference between the actual and the desired activities. Next we change the weight of each connection so as to reduce the error.We repeat this training process for many different images of each different images of each kind of digit until the network classifies every image correctly.To implement this procedure we need to calculate the error derivative for the weight (EW) in order to change the weight by an amount that is proportional to the rate at which the error changes as the weight is changed. One way to calculate the EW is to perturb a weight slightly and observe how the error changes. But that method is inefficient because it requires a separate perturbation for each of the many weights.Another way to calculate the EW is to use the Back-propagation algorithm which is described below, and has become nowadays one of the most important tools for training neural networks. It was developed independently by two teams, one (Fogelman-Soulie, Gallinari and Le Cun) in France, the other (Rumelhart, Hinton and Williams) in U.S.5.3 The Back-Propagation AlgorithmIn order to train a neural network to perform some task, we must adjust the weights of each unit in such a way that the error between the desired output and the actual output is reduced. This process requires that the neural network compute the error derivative of the weights (EW). In other words, it must calculate how the error changes as each weight is increased or decreased slightly. The back propagation algorithm is the most widely used method for determining the EW.The back-propagation algorithm is easiest to understand if all the units in the network are linear. The algorithm computes each EW by first computing the EA, the rate at which the error changes as the activity level of a unit is changed. For output units, the EA is simply the difference between the actual and the desired output. To compute the EA for a hidden unit in the layer just before the output layer, we first identify all the weights between that hidden unit and the output units to which it is connected. We then multiply those weights by the EAs of those output units and add the products. This sum┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊equals the EA for the chosen hidden unit. After calculating all the EAs in the hidden layer just before the output layer, we can compute in like fashion the EAs for other layers, moving from layer to layer in a direction opposite to the way activities propagate through the network. This is what gives back propagation its name. Once the EA has been computed for a unit, it is straight forward to compute the EW for each incoming connection of the unit. The EW is the product of the EA and the activity through the incoming connection.Note that for non-linear units, (see Appendix C) the back-propagation algorithm includes an extra step. Before back-propagating, the EA must be converted into the EI, the rate at which the error changes as the total input received by a unit is changed.6. Applications of neural networks6.1 Neural Networks in PracticeGiven this description of neural networks and how they work, what real world applications are they suited for? Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries.Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including: sales forecastingindustrial process controlcustomer researchdata validationrisk managementtarget marketingBut to give you some more specific examples; ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; hand-written word recognition; and facial recognition.6.2 Neural networks in medicineArtificial Neural Networks (ANN) are currently a 'hot' research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modelling parts of the human body and recognising diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊Neural networks are ideal in recognising diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognise the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the 'quantity'. The examples need to be selected very carefully if the system is to perform reliably and efficiently.6.2.1 Modelling and Diagnosing the Cardiovascular SystemNeural Networks are used experimentally to model the human cardiovascular system. Diagnosis can be achieved by building a model of the cardiovascular system of an individual and comparing it with the real time physiological measurements taken from the patient. If this routine is carried out regularly, potential harmful medical conditions can be detected at an early stage and thus make the process of combating the disease much easier.A model of an individual's cardiovascular system must mimic the relationship among physiological variables (i.e., heart rate, systolic and diastolic blood pressures, and breathing rate) at different physical activity levels. If a model is adapted to an individual, then it becomes a model of the physical condition of that individual. The simulator will have to be able to adapt to the features of any individual without the supervision of an expert. This calls for a neural network.Another reason that justifies the use of ANN technology, is the ability of ANNs to provide sensor fusion which is the combining of values from several different sensors. Sensor fusion enables the ANNs to learn complex relationships among the individual sensor values, which would otherwise be lost if the values were individually analysed. In medical modelling and diagnosis, this implies that even though each sensor in a set may be sensitive only to a specific physiological variable, ANNs are capable of detecting complex medical conditions by fusing the data from the individual biomedical sensors.6.2.2 Electronic nosesANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odours in the remote surgical environment. These identified odours would then be electronically transmitted to another site where an door generation system would recreate them. Because the sense。
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Brain Correlates of Language Learning: The Neuronal Dissociation of Rule-Based versus Similarity-Based Learning Bertram Opitz1 and Angela D. Friederici2 1Experimental Neuropsychology Unit, Saarland University, 66041 Saarbrücken,
Germany, and 2Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04303 Leipzig, Germany
Abstract Language learning is one of the mysteries of human cognition. One of the crucial questions is the following: Does acquisition of grammatical knowledge depend primarily on abstract rules or on item-specific information? Although there is evidence that both mechanisms contribute to language acquisition, their relative importance during the process of language learning is unknown. Using an artificial grammar paradigm, we show by means of functional magnetic resonance imaging that the brain dissociates the two mechanisms: the left anterior hippocampus supports similarity-based learning, whereas the left ventral premotor cortex is selectively engaged by abstract rule processing. Moreover, data analysis over time on learning suggests that similarity-based learning plays a nonobligatory role during the initial phase, and that rule-based abstraction plays a crucial role during later learning.
Key words:hippocampus; rule learning; grammar; premotor cortex; fMRI; similarity
Introduction The processes by which humans learn a language have gained considerable interest over the past years (Hauser et al., 2002). Based on the idea that language is so complex (Chomsky, 1965), the acquisition of grammatical knowledge has been widely assumed to involve structural rules. These so-called phrase structure rules (PSRs) determine how words are combined into phrases and sentences. In contrast, another type of grammar, called finite-state grammar (FSG), is specified by transition probabilities between elements. For the processing of FSGs implemented in artificial grammar-learning tasks (Reber, 1967), a mechanism has been proposed that considers the similarity to exemplars presented during training (Seger, 1994; Shanks, 1995). It has been argued that this form of learning may well explain the acquisition of FSGs. In a number of experiments, Vokey and Brooks (1992) demonstrated that similarity-based learning leads to transfer of grammatical knowledge to a new letter-set in a way similar to rule-based learning. Reconciling the divergent results, it has been proposed that performance in such tasks depends on both item similarity and rule knowledge (Brooks and Vokey, 1991; Knowlton and Squire, 1996). Such proposals necessarily raise the question of how such resources actually interact and which brain systems mediate them.
It is commonly agreed that rule-based learning of natural grammars is mediated by the left prefrontal cortex, especially Broca's area (Musso et al., 2003; Opitz and Friederici, 2003), because it is the rule-based processing center of acquired syntax (Ullman et al., 1997; Indefrey et al., 2001). With respect to similarity-based learning mechanisms, there is less converging evidence. Recent studies that use functional magnetic resonance imaging (fMRI) have proposed the role of the left hippocampus in similarity-based learning (Strange et al., 2001; Opitz and Friederici, 2003). In the study by Strange et al. (2001), subjects made grammaticality judgments to letter strings for which the governing rule and the letters that comprised the exemplars were periodically changed. Activity in the left hippocampus was modulated by changes of exemplars, but not by changes of grammatical rules. However, FSG learning based on exemplar-specific information has been shown to be intact in patients suffering from global amnesia and thus to be independent of the hippocampal system (Knowlton and Squire, 1996).
To investigate the neural basis of both learning mechanisms, we created an artificial grammar consisting of PSRs and a small vocabulary. Once learned, participants were presented with language input, which was systematically changed in terms of either the phrase structure rules or one feature of a particular word category. This design of this fMRI study allowed us to evaluate modulations of brain activity as a function of the two types of changes while keeping everything else constant. If the hippocampal formation plays a prominent role in similarity-based learning, it should be activated by changes of words in a particular category. In contrast, changes of the underlying phrase structure rules might modulate activation in the left prefrontal cortex.