Developing and validating computational models of working memory in the human prefrontal cortex
开发和验证人类前额皮质工作记忆的计算模型
基本信息
- 批准号:RGPIN-2021-03035
- 负责人:
- 金额:$ 2.04万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2021
- 资助国家:加拿大
- 起止时间:2021-01-01 至 2022-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
We perceive the world around us in the form of a continuous stream that enters our mind through the lens of our senses. At every moment we can only see, hear and feel bits and pieces of our surroundings, yet we constantly demonstrate behaviors that reach far past the simple reflexive reactions. Memory is inarguably an essential component of our ability to bind information over long time spans. The human brain is able to memorize experiences through several types of memory systems that differ depending on the temporal and contextual requirements of the behavior. One of the critical memory systems is working memory - the ability to store and manipulate information over short periods. The long-term goal of research in my group is to specify how the human brain maintains and organizes the previous experiences in memory to make them readily accessible for rapid decision making. My group uses a combination of experimental and computational techniques to build mechanistic models of the neural computations underlying memory in the human brain. To do this, we focus on 1) developing benchmarks from human brains responses to quantify the progress towards building such models; 2) developing computational models with the goal of maximizing their scores against the neural benchmarks. The overall goal of this proposal is to apply our modeling approach towards understanding the neural computations underlying working memory in the human prefrontal cortex. Specifically 1) we use neuroimaging techniques to characterize the brain response patterns during various memory-dependent tasks. We then use this data to construct benchmarks from neural responses in the prefrontal cortex; 2) we form scientific hypotheses in the form of computational models by making specific choices of model architecture, learning objective, and environment composition. 3) we validate the correctness of these hypotheses by validating them against the neural benchmarks. Computational models are becoming increasingly important in understanding physiological systems. These mechanistic models allow us to encapsulate our integral knowledge of a system into a framework that may not be verbally expressive. While current theories of the prefrontal cortex highlight its role in tracking goals and ways to achieve them, they do not offer mechanistic explanations as to how such information may be represented in the population of neuronal responses. This application proposes a computational framework for developing mechanistic models of the neural computations underlying working memory in the prefrontal cortex and tackles the structural, developmental, and ecological aspects of such models. The resulting mechanistic models with known connectivity are invaluable for understanding the neural computations underlying working memory in the prefrontal cortex.
我们以源源不断的流的形式感知周围的世界,通过我们的感官镜头进入我们的大脑。每时每刻,我们只能看到、听到和感觉到我们周围的点点滴滴,但我们不断地展示出远远超出简单的反射反应的行为。毫无疑问,记忆是我们在很长一段时间内绑定信息的能力的重要组成部分。人脑能够通过几种不同类型的记忆系统记忆经验,这些记忆系统根据行为的时间和背景要求而有所不同。关键的记忆系统之一是工作记忆--在短时间内存储和处理信息的能力。我所在团队的长期研究目标是明确人脑如何在记忆中保持和组织先前的经验,以便快速做出决策。我的团队使用实验和计算技术相结合的方法来建立人脑中潜在记忆的神经计算的机械模型。为此,我们专注于1)从人脑反应中开发基准,以量化构建此类模型的进展;2)开发计算模型,目标是根据神经基准最大化它们的分数。这项提议的总体目标是应用我们的建模方法来理解人类前额叶皮质工作记忆的神经计算。具体地说,1)我们使用神经成像技术来表征在各种记忆依赖任务中的大脑反应模式。然后,我们使用这些数据来根据前额叶皮质的神经反应构建基准;2)我们通过对模型架构、学习目标和环境组成的具体选择,以计算模型的形式形成科学假设。3)我们通过对照神经基准来验证这些假设的正确性。计算模型在理解生理系统方面正变得越来越重要。这些机械模型允许我们将我们对系统的完整知识封装到一个可能无法用语言表达的框架中。虽然目前关于前额叶皮质的理论强调了它在跟踪目标和实现目标的方法中的作用,但它们并没有提供机制上的解释,说明这些信息如何在神经元反应的群体中表现出来。这项申请提出了一个计算框架,用于开发前额叶皮质工作记忆潜在的神经计算的机械模型,并解决了此类模型的结构、发育和生态方面的问题。由此产生的具有已知连接性的机械模型对于理解前额叶皮质工作记忆背后的神经计算是非常宝贵的。
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Bashivan, Pouya其他文献
Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity
- DOI:
10.1111/ejn.12749 - 发表时间:
2014-12-01 - 期刊:
- 影响因子:3.4
- 作者:
Bashivan, Pouya;Bidelman, Gavin M.;Yeasin, Mohammed - 通讯作者:
Yeasin, Mohammed
NEUROSCIENCE Neural population control via deep image synthesis
- DOI:
10.1126/science.aav9436 - 发表时间:
2019-05-03 - 期刊:
- 影响因子:56.9
- 作者:
Bashivan, Pouya;Kar, Kohitij;DiCarlo, James J. - 通讯作者:
DiCarlo, James J.
Bashivan, Pouya的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Bashivan, Pouya', 18)}}的其他基金
Developing and validating computational models of working memory in the human prefrontal cortex
开发和验证人类前额皮质工作记忆的计算模型
- 批准号:
RGPIN-2021-03035 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Developing and validating computational models of working memory in the human prefrontal cortex
开发和验证人类前额皮质工作记忆的计算模型
- 批准号:
DGECR-2021-00300 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Launch Supplement
相似海外基金
Developing and validating computational models of working memory in the human prefrontal cortex
开发和验证人类前额皮质工作记忆的计算模型
- 批准号:
RGPIN-2021-03035 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Developing and validating computational models of working memory in the human prefrontal cortex
开发和验证人类前额皮质工作记忆的计算模型
- 批准号:
DGECR-2021-00300 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Launch Supplement
Validating the Flavivirus Envelope Protein as an Antiviral Target
验证黄病毒包膜蛋白作为抗病毒靶点
- 批准号:
10338189 - 财政年份:2020
- 资助金额:
$ 2.04万 - 项目类别:
Validating the Flavivirus Envelope Protein as an Antiviral Target
验证黄病毒包膜蛋白作为抗病毒靶点
- 批准号:
10578759 - 财政年份:2020
- 资助金额:
$ 2.04万 - 项目类别:
Validating the Flavivirus Envelope Protein as an Antiviral Target
验证黄病毒包膜蛋白作为抗病毒靶点
- 批准号:
10413666 - 财政年份:2020
- 资助金额:
$ 2.04万 - 项目类别:
Developing and validating a computational model of the gut microbiota-mucosa interactions to replace and reduce animal experiments
开发和验证肠道微生物群-粘膜相互作用的计算模型,以取代和减少动物实验
- 批准号:
NC/R001707/1 - 财政年份:2018
- 资助金额:
$ 2.04万 - 项目类别:
Training Grant
Developing and validating a computational model of the gut microbiota-mucosa interactions to replace and reduce animal experiments
开发和验证肠道微生物群-粘膜相互作用的计算模型,以取代和减少动物实验
- 批准号:
2103295 - 财政年份:2018
- 资助金额:
$ 2.04万 - 项目类别:
Studentship
Validating engineered hiPSC-derived cardiomyocytes as model cells
验证工程化 hiPSC 衍生心肌细胞作为模型细胞
- 批准号:
9030330 - 财政年份:2016
- 资助金额:
$ 2.04万 - 项目类别:
Validating Machine-Learned Classifiers of Sedentary Behavior and Physical Activit
验证久坐行为和身体活动的机器学习分类器
- 批准号:
8371173 - 财政年份:2012
- 资助金额:
$ 2.04万 - 项目类别:
COMPUTATIONAL AND FUNCTIONAL APPROACHES TO VALIDATING CANCER GENOME TARGETS
验证癌症基因组目标的计算和功能方法
- 批准号:
8593329 - 财政年份:2012
- 资助金额:
$ 2.04万 - 项目类别: