Dendritic Computation and the Neural Code
树突计算和神经代码
基本信息
- 批准号:RGPIN-2017-06872
- 负责人:
- 金额:$ 1.89万
- 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:2020
- 资助国家:加拿大
- 起止时间:2020-01-01 至 2021-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
If one connects loudspeakers to an electrode implanted in brain cells, one would hear a rough, crackling and apparently unstructured sound. Is this noise a reflection of intrinsic imprecisions of an organic form of information processing or, rather, is it the result of an unknown and perhaps highly optimized way of encoding information? At the center of neuroscience research lies this problem of neural coding. It has become clear that peripheral nerves code information streaming from the senses in their spiking rate. Neurons within the hierarchical structure of the neocortex, however, constantly combine information of two different natures: bottom-up information coming more directly from the senses and top-down information coming from internal sources. Therefore, we propose a simple reformulation of the neural coding problem and ask the general question: How can a single population of neurons encode two streams of information simultaneously? Recent experimental evidence point to a pivotal role of dendrites in answering this question.
Using numerical simulations of neocortical networks, this grant will (1) determine the role of dendrite-dependent bursting for representing top-down and bottom-up information simultaneously. In addition, the simulations will be used to (2) investigate the role of inhibitory connection motifs to optimize the bursting neural code. Lastly, we will (3) develop statistical data analysis methods to facilitate experimental investigations of dendrite-dependent burst coding.
The most powerful machine learning method of today, deep learning, was inspired by the hierarchical structure of the neocortex. By outlining the rules for neural coding in a hierarchy, the proposed work can inspire efficient implementations of signal processing algorithms. In addition, understanding the neural code used by the neocortex is essential to the analysis of biomedical data. To single out a possible area of application, we note that the improvement of brain-machine interface technology strongly depends on novel decoding algorithms of the type discussed in this proposal. Therefore, our novel approach to the problem of neural coding can lead to valuable technologies.
如果把扬声器和植入脑细胞的电极连接起来,就会听到一种粗糙的、噼啪作响的、明显没有结构的声音。这种噪音是否反映了信息处理有机形式的内在不精确,或者更确切地说,它是一种未知的、可能是高度优化的信息编码方式的结果?神经科学研究的核心是神经编码问题。现在已经很清楚,周围神经以它们的尖峰速率编码来自感官的信息。然而,新皮层层次结构中的神经元不断地将两种不同性质的信息组合在一起:一种是直接来自感官的自下而上的信息,另一种是来自内部来源的自上而下的信息。因此,我们提出了一个简单的神经编码问题的重新表述,并提出了一个一般性的问题:单个神经元群体如何同时编码两个信息流?最近的实验证据表明树突在回答这个问题方面起着关键作用。
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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Naud, Richard其他文献
Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation
- DOI:
10.1016/j.neuroscience.2021.07.026 - 发表时间:
2022-04-21 - 期刊:
- 影响因子:3.3
- 作者:
Harkin, Emerson F.;Shen, Peter R.;Naud, Richard - 通讯作者:
Naud, Richard
Parsing Out the Variability of Transmission at Central Synapses Using Optical Quantal Analysis
- DOI:
10.3389/fnsyn.2019.00022 - 发表时间:
2019-08-14 - 期刊:
- 影响因子:3.7
- 作者:
Soares, Cary;Trotter, Daniel;Naud, Richard - 通讯作者:
Naud, Richard
Firing patterns in the adaptive exponential integrate-and-fire model.
- DOI:
10.1007/s00422-008-0264-7 - 发表时间:
2008-11 - 期刊:
- 影响因子:1.9
- 作者:
Naud, Richard;Marcille, Nicolas;Clopath, Claudia;Gerstner, Wulfram - 通讯作者:
Gerstner, Wulfram
Counting on dis-inhibition: a circuit motif for interval counting and selectivity in the anuran auditory system
- DOI:
10.1152/jn.00138.2015 - 发表时间:
2015-11-01 - 期刊:
- 影响因子:2.5
- 作者:
Naud, Richard;Houtman, Dave;Longtin, Andre - 通讯作者:
Longtin, Andre
Speed-invariant encoding of looming object distance requires power law spike rate adaptation
- DOI:
10.1073/pnas.1306428110 - 发表时间:
2013-08-13 - 期刊:
- 影响因子:11.1
- 作者:
Clarke, Stephen E.;Naud, Richard;Maler, Leonard - 通讯作者:
Maler, Leonard
Naud, Richard的其他文献
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{{ truncateString('Naud, Richard', 18)}}的其他基金
Dendritic Computation and the Neural Code
树突计算和神经代码
- 批准号:
RGPIN-2017-06872 - 财政年份:2022
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Dendritic Computation and the Neural Code
树突计算和神经代码
- 批准号:
RGPIN-2017-06872 - 财政年份:2021
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Dendritic Computation and the Neural Code
树突计算和神经代码
- 批准号:
RGPIN-2017-06872 - 财政年份:2019
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Dendritic Computation and the Neural Code
树突计算和神经代码
- 批准号:
RGPIN-2017-06872 - 财政年份:2018
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Dendritic Computation and the Neural Code
树突计算和神经代码
- 批准号:
RGPIN-2017-06872 - 财政年份:2017
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Développement d'une méthode efficace pour la perforation des cellules du coeur des panneaux en matériaux composite à nid d'abeille pour une application spatiale
开发复合材料中的细胞穿孔方法和应用空间
- 批准号:
365414-2008 - 财政年份:2008
- 资助金额:
$ 1.89万 - 项目类别:
Experience Awards (previously Industrial Undergraduate Student Research Awards)
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