  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.
