论文标题
适应性允许识别优化的神经代码
Adaptation Properties Allow Identification of Optimized Neural Codes
论文作者
论文摘要
有效的编码方法可以很好地捕获神经代码对其环境统计数据的适应。在这里，我们解决了一个反问题：表征有效代码似乎最佳的目标和约束函数，基于它们如何适应不同的刺激分布。我们制定了一个通用有效的编码问题，具有灵活的目标和约束功能以及最小的参数假设。解决该模型的特殊情况，我们为基于Fisher信息的大量有效编码问题提供解决方案，从而推广了一系列以前的结果。我们表明，不同的目标函数类型在质量上施加了不同的适应行为，而约束则强制执行特征偏离经典的高效编码特征。尽管这些效果之间的相互作用相互作用，但出现了无约束优化问题和信息最大化目标功能的明显签名。要求对神经代码适应的定点，我们发现对神经代码的约束独立表征。我们使用此结果提出了一个实验范式，该范式可以表征观察到的代码似乎已优化的目标和约束函数。
The adaptation of neural codes to the statistics of their environment is well captured by efficient coding approaches. Here we solve an inverse problem: characterizing the objective and constraint functions that efficient codes appear to be optimal for, on the basis of how they adapt to different stimulus distributions. We formulate a general efficient coding problem, with flexible objective and constraint functions and minimal parametric assumptions. Solving special cases of this model, we provide solutions to broad classes of Fisher information-based efficient coding problems, generalizing a wide range of previous results. We show that different objective function types impose qualitatively different adaptation behaviors, while constraints enforce characteristic deviations from classic efficient coding signatures. Despite interaction between these effects, clear signatures emerge for both unconstrained optimization problems and information-maximizing objective functions. Asking for a fixed-point of the neural code adaptation, we find an objective-independent characterization of constraints on the neural code. We use this result to propose an experimental paradigm that can characterize both the objective and constraint functions that an observed code appears to be optimized for.
