EAGER: Noise and strong analog error-correcting codes in neural computation

该项目旨在揭示存在比以前已知的大脑中更好的模拟纠错码，展示如何使用和解码这些代码，并开发量化这些代码性能的理论。 信息理论被引入神经科学相对较早，有效（源）编码理论已被广泛接受的感觉神经科学。然而，奇怪的是，信息论的第二个分支，即处理最大限度地节省冗余以从噪声中恢复信号的理论，在神经科学中没有取得进展。香农的信道编码定理揭示了编码的存在，这种编码可以以以前认为不可能的效率进行纠错。研究者的中心假设是，大脑经常使用这种纠错码以及解码和使用它们所需的机器。该假设的动机是研究人员及其同事最近对动物位置的网格细胞代码进行的分析，显示与大脑中已知的群体代码相比，它具有前所未有的纠错特性（Sreenivasan Fiete，2011）。研究者建议：1）为模拟神经代码开发定义和约束，以将信道编码框架应用于神经代码，从而表征它们在纠错方面的“优良性”。2)识别能够实现强纠错的高级编码属性，并在观察到但理解不深的神经代码中搜索这些属性。与此同时，探索强有力的理论纠错代码，大脑可能会合理地执行。3)为解码这些代码建立合理的神经机制模型。解码是推理，所以这个问题可以更普遍地被认为是探索分层推理的神经机制。该项目是计算和理论的，还涉及与神经生理学家的密切合作，将量化技术应用于神经数据，并与实验一起工作，为理论和测试预测提供信息。

This project aims to uncover the existence of a qualitatively better class of analog error-correcting codes than previously known in the brain, show how such codes can be used and decoded, and develop the theory for quantifying the performance of such codes. Information theory was introduced into neuroscience relatively early, and the theory of efficient (source) coding has been widely embraced in the sensory neurosciences. However, the second branch of information theory, which deals with the maximally parsimonious addition of redundancy to recover signal from noise, has curiously not made inroads in neuroscience. Shannon's channel coding theorem revealed the existence of codes that make possible error correction at efficiencies previously thought impossible.The investigator's central hypothesis is that the brain routinely employs such error correcting codes and the machinery required to decode and work with them. The hypothesis is motivated by a recent analysis of the grid cell code for animal location by the investigator and colleagues, showing it has unprecedented error-correction properties compared to known population codes in the brain (Sreenivasan & Fiete, 2011). The investigator proposes to: 1) Develop definitions and constraints for analog neural codes, to apply the channel coding framework to neural codes and thus characterize their "goodness" on error-correction. 2) Identify high-level coding properties that enable strong error-correction, and search for these properties in observed but poorly understood neural codes. At the same time, explore strong theoretical error-correcting codes that the brain may plausibly implement. 3) Model plausible neural mechanisms for decoding such codes. Decoding is inference, so this question can be more generally thought of as exploring neural mechanisms for hierarchical inference. This project is computational and theoretical, and also involves close collaboration with neurophysiologists, to apply quantification techniques to neural data and work with experiments to inform the theories and test predictions.