Dendritic Computation and the Neural Code

树突计算和神经代码

• 批准号: RGPIN-2017-06872
• 负责人: Naud, Richard
• 金额: $ 3.79万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746586
• 关键词: Dendritic; Computation; Neural; Code

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.

如果将扬声器连接到植入脑细胞的电极上，人们会听到一种粗糙的、噼啪作响的、明显无结构的声音。这种噪音是反映了信息处理的有机形式的内在不精确性，还是一种未知的，也许是高度优化的信息编码方式的结果？神经科学研究的中心是神经编码问题。很明显，周围神经以其尖峰速率编码来自感官的信息流。然而，在新皮层的层级结构中，神经元不断地将两种不同性质的信息联合收割机结合起来：自下而上的信息更直接地来自感官，而自上而下的信息来自内部来源。因此，我们提出了一个简单的神经编码问题的重新表述，并提出了一个普遍的问题：一个单一的神经元群体如何同时编码两个信息流？最近的实验证据指出树突在回答这个问题中扮演着关键的角色。使用新皮层网络的数值模拟，本基金将（1）确定树突依赖的爆发在同时表示自上而下和自下而上信息中的作用。此外，模拟将用于（2）研究抑制性连接基序在优化突发神经代码中的作用。最后，我们将（3）开发统计数据分析方法，以促进树突依赖的突发编码的实验研究。当今最强大的机器学习方法，深度学习，受到新皮层分层结构的启发。通过在层次结构中概述神经编码的规则，所提出的工作可以激发信号处理算法的有效实现。此外，理解新皮层使用的神经代码对于分析生物医学数据至关重要。为了挑出一个可能的应用领域，我们注意到，脑机接口技术的改进在很大程度上取决于本提案中讨论的新型解码算法。因此，我们解决神经编码问题的新方法可以带来有价值的技术。