# Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?

## TL;DR

This paper evaluates the effectiveness of transfer entropy in inferring causal information flow in neural circuits, revealing its limitations and task-dependent accuracy in complex cognitive processes.

## Contribution

It demonstrates that transfer entropy can both fail and falsely indicate causality in neural systems, highlighting the need to understand underlying logic in cognitive information flow.

## Key findings

- Transfer entropy sometimes fails to detect existing causal relations.
- Transfer entropy can incorrectly suggest causality where none exists.
- The accuracy of transfer entropy inference varies with the cognitive task.

## Abstract

To infer information flow in any network of agents, it is important first and foremost to establish causal temporal relations between the nodes. Practical and automated methods that can infer causality are difficult to find, and the subject of ongoing research. While Shannon information only detects correlation, there are several information-theoretic notions of "directed information" that have successfully detected causality in some systems, in particular in the neuroscience community. However, recent work has shown that some directed information measures can sometimes inadequately estimate the extent of causal relations, or even fail to identify existing cause-effect relations between components of systems, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks: motion detection and sound localization. We also test whether activity time-series recorded from behaving digital brains can infer information flow using the transfer entropy concept, when compared to a ground-truth model of causal influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer causality when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system.