Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in neural dynamics. A prime example are studies on predictive coding. These often ask if confirmed predictions about inputs or predictions errors between internal predictions and inputs are passed on in a hierarchical neural system--while at the same time looking for the neural correlates of coding for errors and predictions. If we do not know exactly what a neural system predicts at any given moment, this results in a circular analysis--as has been criticized correctly. To circumvent such circular analysis, we propose to express information processing strategies (such as predictive coding) by local information-theoretic quantities, such that they can be estimated directly from neural data. We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy. We define testable hypotheses on the relationship of both quantities to identify which of the assumed strategies was used. We demonstrate our approach on spiking data from the retinogeniculate synapse of the cat. Applying our local information dynamics framework, we are able to show that the synapse codes for predictable rather than surprising input. To support our findings, we apply measures from partial information decomposition, which allow to differentiate if the transferred information is primarily bottom-up sensory input or information transferred conditionally on the current state of the synapse. Supporting our local information-theoretic results, we find that the synapse preferentially transfers bottom-up information.

翻译：研究神经信息处理的研究往往隐含地同时探讨两个问题：多种备选处理策略中采用了哪一种，以及该策略在神经动态中如何实现。预测编码研究是典型例子。这类研究常探究：在层级神经系统中，关于输入的确认预测，还是内部预测与输入之间的预测误差被传递——同时寻找编码误差与预测的神经相关性。若无法确知神经系统的即时预测内容，则会导致循环分析——这一批评恰如其分。为规避此类循环分析，我们提出用局部信息论量表达信息处理策略（如预测编码），使其可直接从神经数据中估计。我们通过研究两种对立的预测编码类处理策略来展示该方法，利用局部主动信息存储和局部转移熵量化预测编码的基本构件——即输入的可预测性与信息传递。我们定义了关于这两个量之间关系的可检验假设，以识别所采用的假设策略。我们在猫视网膜-膝状体突触的脉冲数据上演示了该方法。应用我们的局部信息动力学框架，我们能够证明该突触编码的是可预测输入而非意外输入。为支持这一发现，我们采用了部分信息分解的度量，以区分传递的信息主要是自下而上的感觉输入，还是基于突触当前状态有条件传递的信息。这支持了我们的局部信息论结果，我们发现该突触优先传递自下而上的信息。