将低维动力学映射到高维神经活动：从神经工程框架导出环模型
Mapping low-dimensional dynamics to high-dimensional neural activity: A derivation of the ring model from the neural engineering framework
摘要：
经验估计的神经元群体活动的维度往往远低于人口规模。在训练和设计的神经网络模型中也观察到类似的现象。这些实验和计算结果表明，映射低维动力学高维神经空间是一个共同的特点，皮层计算。尽管这种观察普遍存在，但对这种映射所产生的约束却知之甚少。在这里，我们考虑将低维动力学映射到高维神经活动的一个具体例子--神经工程框架。我们解析地解决了经典环模型的框架-一个神经网络编码的静态或动态的角度变量。我们的研究结果提供了一个完整的表征成功和失败的模式，这个模型。基于这个框架和其他框架之间的相似性，我们推测这些结果可以适用于更一般的场景。
英文摘要：
Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity -- the neural engineering framework. We analytically solve the framework for the classic ring model -- a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
