Towards a neural implementation of causal inference in cue combination

Causal inference in sensory cue combination is the process of determining whether multiple sensory cues have the same cause or different causes. Psychophysical evidence indicates that humans closely follow the predictions of a Bayesian causal inference model. Here, we explore how Bayesian causal inference could be implemented using probabilistic population coding and plausible neural operations, but conclude that the resulting architecture is unrealistic.