Dendritic Computation and the Neural Code

如果一个人将扬声器连接到植入脑细胞的电极上，就会听到粗糙、嘎吱作响和明显无结构的声音。这种噪音是反映了一种有机形式的信息处理的内在不精确性，还是，更确切地说，它是一种未知的、或许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的问题。很明显，外周神经以其尖峰频率对从感官流出的信息进行编码。然而，新皮质层级结构中的神经元不断地结合两种不同性质的信息：自下而上的信息更直接地来自感觉，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？最近的实验证据指出了树突在回答这个问题中的关键作用。*通过对新皮质网络的数值模拟，这项研究将(1)确定树突依赖的爆裂在同时表示自上而下和自下而上信息方面的作用。此外，这些模拟将被用来(2)研究抑制连接基序在优化突发性神经编码中的作用。最后，我们将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，灵感来自于大脑皮层的层次结构。通过在层次中概述神经编码的规则，所提出的工作可以启发信号处理算法的有效实现。此外，了解新大脑皮层使用的神经密码对于分析生物医学数据是必不可少的。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依赖于本提案中讨论的类型的新型解码算法。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。

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.