Bayesian and Decision Theoretic Tools
贝叶斯和决策理论工具
基本信息
- 批准号:8380915
- 负责人:
- 金额:$ 7.64万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2003
- 资助国家:美国
- 起止时间:2003-06-15 至
- 项目状态:未结题
- 来源:
- 关键词:AccountingAffectAgingAlgorithmsAnimal BehaviorAnimalsAreaBayesian MethodBehaviorBehavioralBooksBrainBrain InjuriesClassificationCodeCommunitiesDataData AnalysesDecision MakingDecision TheoryEconomicsEventHandHumanImageInstructionKnowledgeLaboratoriesLearningLinear ModelsMeasurementMeasuresMeleagris gallopavoMethodsModelingMotorMovementNervous system structureNeuronsNoisePositioning AttributePostdoctoral FellowProbabilityProbability LearningProductionPropertyPublishingResearchResearch SupportRoboticsSignal TransductionSiteStrokeStudentsSystemSystems AnalysisTechniquesTravelUncertaintyUpdateWorkbasedata managementdata miningdata modelingdata sharingfeedinginterestmillisecondneural modelnovelprogramsrelating to nervous systemresearch studysequence learningtheoriestherapy designtool
项目摘要
Core B: Bayesian and decision theoretic tools
The production of movement sequences is inherently affected by uncertainty: to move rapidly the animal needs to
estimate what to do next given past knowledge. Such estimates can never be certain. A colorful example from a
recently popular book (Taleb, 2008) shows that we can never be certain about a sequence of events. The turkey
that has been fed every day for close to a year gets slaughtered for Thanksgiving. Many communities such as
robotics, economics, data mining and models of human behavior are converging on a common approach towards
formalizing uncertainty: Bayesian decision theory. We will first use these methods to predict behaviors from each
of the three experimental labs. We will continue to extract the relevant variables (timescales, probabilities) that
need to be represented by the nervous system to efficiently produce sequences. These variables will then be
correlated with measured neural signals to ask how these variables are represented.
Moreover, uncertainty is central when analyzing data from neurons. When we are asking how neurons store and
recall motor sequences we never directly measure the relevant variables, such as niemory, we rather measure spikes
or imaging signals that are affected by noise. A central topic for neural data analysis, therefore, is to combine
many measurements (say 1000 spikes) into an estimate (of say tuning properties) that has small uncertainty (or
narrow error-bars). We will use state of the art Bayesian data analysis techniques to analyze the data resulting from
the proposed experiments in the other projects. Specifically we are interested in asking how neurons interact with
one another using these Bayesian methods. Lastly, we will use state of the art decoding methods to ask how well
various types of information are encoded by the measured signals. This is useful for the experimental projects as it
allows asking how much information about a, variable of interest is encoded by neural signals.
RELEVANCE (See instructions):
The proposed work is central to the problem of understanding the mechansims where practice leads to to
reorganizafion of the human motor system in the face of aging, neurodenerafion, stroke or brain injury.
Understanding these mechansims has an impact on the design of therapies directed at preserving function,
developing compensator movements and ulfimately, developing novel motor capacity.
核心B:贝叶斯和决策理论工具
动作序列的产生天生就受到不确定性的影响:为了快速移动,动物需要
根据过去的知识估计下一步要做什么。这样的估计永远不可能确定。一个丰富多彩的例子
最近流行的一本书(Taleb,2008)表明,我们永远不能确定一系列事件。火鸡
近一年来每天喂食的鱼在感恩节被宰杀。许多社区,如
机器人学、经济学、数据挖掘和人类行为模型正汇聚在一个共同的方法上,以实现
不确定性的形式化:贝叶斯决策理论。我们将首先使用这些方法来预测每个
三个实验实验室中。我们将继续提取相关变量(时间尺度、概率)
需要由神经系统来代表才能有效地产生序列。然后这些变量将是
与测量的神经信号相关联,以询问这些变量是如何表示的。
此外,在分析来自神经元的数据时,不确定性是核心问题。当我们问神经元是如何存储和
回忆运动序列我们从不直接测量相关变量,例如记忆,我们测量的是尖峰
或受噪声影响的成像信号。因此,神经数据分析的一个中心主题是将
许多测量值(例如1000个峰值)进入具有小不确定性(或
窄误差条)。我们将使用最先进的贝叶斯数据分析技术来分析
建议在其他项目中进行的实验。具体地说,我们感兴趣的是神经元是如何与
使用这些贝叶斯方法。最后,我们将使用最先进的解码方法来询问
通过测量信号对各种类型的信息进行编码。这对实验项目是有用的,因为它
允许询问神经信号编码了多少有关感兴趣变量的信息。
相关性(请参阅说明):
拟议的工作是理解实践导致的机制的核心问题
面对衰老、神经退化、中风或脑损伤时人类运动系统的重组。
了解这些机制对旨在保护功能的疗法的设计有影响,
开发补偿器运动,最终开发新的电机容量。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Konrad P. Kording其他文献
Causal inference on human behaviour
关于人类行为的因果推断
- DOI:
10.1038/s41562-024-01939-z - 发表时间:
2024-08-23 - 期刊:
- 影响因子:15.900
- 作者:
Drew H. Bailey;Alexander J. Jung;Adriene M. Beltz;Markus I. Eronen;Christian Gische;Ellen L. Hamaker;Konrad P. Kording;Catherine Lebel;Martin A. Lindquist;Julia Moeller;Adeel Razi;Julia M. Rohrer;Baobao Zhang;Kou Murayama - 通讯作者:
Kou Murayama
Individual-specific strategies inform category learning
- DOI:
10.1038/s41598-024-82219-8 - 发表时间:
2025-01-23 - 期刊:
- 影响因子:3.900
- 作者:
Jared S. Collina;Gozde Erdil;Mingyi Xia;Christopher F. Angeloni;Katherine C. Wood;Janaki Sheth;Konrad P. Kording;Yale E. Cohen;Maria N. Geffen - 通讯作者:
Maria N. Geffen
Measuring Causal Effects of Civil Communication without Randomization
在非随机化的情况下测量民间传播的因果效应
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Tony Liu;Lyle Ungar;Konrad P. Kording;Morgan McGuire - 通讯作者:
Morgan McGuire
The interplay of uncertainty, relevance and learning influences auditory categorization
不确定性、相关性和学习之间的相互作用影响听觉分类。
- DOI:
10.1038/s41598-025-86856-5 - 发表时间:
2025-01-27 - 期刊:
- 影响因子:3.900
- 作者:
Janaki Sheth;Jared S. Collina;Eugenio Piasini;Konrad P. Kording;Yale E. Cohen;Maria N. Geffen - 通讯作者:
Maria N. Geffen
A Probabilistic Model of Meetings That Combines Words and Discourse Features
结合词语和话语特征的会议概率模型
- DOI:
10.1109/tasl.2008.925867 - 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Mike Dowman;Virginia Savova;Thomas L. Griffiths;Konrad P. Kording;J. B. Tenenbaum;Matthew Purver - 通讯作者:
Matthew Purver
Konrad P. Kording的其他文献
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{{ truncateString('Konrad P. Kording', 18)}}的其他基金
Grassroots Rigor: making rigorous research practices accessible, meaningful, and building a community around them
草根严谨:使严格的研究实践变得可行、有意义,并围绕它们建立一个社区
- 批准号:
10673711 - 财政年份:2022
- 资助金额:
$ 7.64万 - 项目类别:
Grassroots Rigor: making rigorous research practices accessible, meaningful, and building a community around them
草根严谨:使严格的研究实践变得可行、有意义,并围绕它们建立一个社区
- 批准号:
10513441 - 财政年份:2022
- 资助金额:
$ 7.64万 - 项目类别:
Massive scale electrical neural recordings in vivo using commercial ROIC chips
使用商用 ROIC 芯片进行大规模体内电神经记录
- 批准号:
9558974 - 财政年份:2017
- 资助金额:
$ 7.64万 - 项目类别:
LifeSense: Transforming Behavioral Assessment of Depression Using Personal Sensing Technology
LifeSense:利用个人感知技术改变抑郁症的行为评估
- 批准号:
9982127 - 财政年份:2017
- 资助金额:
$ 7.64万 - 项目类别:
Massive scale electrical neural recordings in vivo using commercial ROIC chips
使用商用 ROIC 芯片进行大规模体内电神经记录
- 批准号:
9011964 - 财政年份:2015
- 资助金额:
$ 7.64万 - 项目类别:
Massive scale electrical neural recordings in vivo using commercial ROIC chips
使用商用 ROIC 芯片进行大规模体内电神经记录
- 批准号:
9146823 - 财政年份:2015
- 资助金额:
$ 7.64万 - 项目类别:
Neural Mechanisms of Fixation Choice while Searching Natural Scenes
搜索自然场景时注视选择的神经机制
- 批准号:
8297707 - 财政年份:2012
- 资助金额:
$ 7.64万 - 项目类别:
Neural Mechanisms of Fixation Choice while Searching Natural Scenes
搜索自然场景时注视选择的神经机制
- 批准号:
8451290 - 财政年份:2012
- 资助金额:
$ 7.64万 - 项目类别:
Neural Mechanisms of Fixation Choice while Searching Natural Scenes
搜索自然场景时注视选择的神经机制
- 批准号:
8634100 - 财政年份:2012
- 资助金额:
$ 7.64万 - 项目类别:
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