论文标题
将低维动力学映射到高维神经活动：源自神经工程框架的环模型
Mapping low-dimensional dynamics to high-dimensional neural activity: A derivation of the ring model from the neural engineering framework
论文作者
论文摘要
神经人口活动的维度的经验估计通常远低于人口规模。在训练和设计的神经网络模型中也观察到类似现象。这些实验和计算结果表明，将低维动力学映射到高维神经空间是皮质计算的共同特征。尽管这一观察结果无处不在，但对这种映射产生的约束知之甚少。在这里，我们考虑了将低维动力学映射到高维神经活动的特定示例 - 神经工程框架。我们通过分析求解经典环模型的框架 - 一种编码静态或动态角变量的神经网络。我们的结果提供了该模型成功和故障模式的完整表征。基于该框架与其他框架之间的相似性，我们推测这些结果可能适用于更一般的方案。
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.
