Sampling-based Causal Inference in Cue Combination and its Neural Implementation
This work addresses the challenge of making causal inference models more realistic and generalizable for neuroscience applications, though it is incremental in improving upon existing methods.
The paper tackles the problem of implementing Bayesian causal inference for cue combination in neural circuits, proposing a hierarchical importance sampling algorithm that converges to accurate values with infinite samples and a generalizable neural circuit design.
Causal inference in cue combination is to decide whether the cues have a single cause or multiple causes. Although the Bayesian causal inference model explains the problem of causal inference in cue combination successfully, how causal inference in cue combination could be implemented by neural circuits, is unclear. The existing method based on calculating log posterior ratio with variable elimination has the problem of being unrealistic and task-specific. In this paper, we take advantages of the special structure of the Bayesian causal inference model and propose a hierarchical inference algorithm based on importance sampling. A simple neural circuit is designed to implement the proposed inference algorithm. Theoretical analyses and experimental results demonstrate that our algorithm converges to the accurate value as the sample size goes to infinite. Moreover, the neural circuit we design can be easily generalized to implement inference for other problems, such as the multi-stimuli cause inference and the same-different judgment.