A Novel KL Divergence Optimization Method for Aligning Neural Population Patterns during Task Learning

Numerous studies suggest that learning related but different tasks prior to a new task makes it easier, possibly because of our brain's neural pattern alignment mechanism. Specifically, the neural patterns in the new task align with those in the learned task, enabling the reuse of knowledge from the previous task to aid learning in the new task. Brain-machine interface (BMI) is an excellent tool for analyzing the dynamics of neural population patterns during new task learning by directly recording neural signals from the brain. If we can repeat the process of aligning neural pattern using a point registration algorithm with the recorded neural signals, it would provide a computational tool to help us understand the brain mechanism during task learning. Additionally, the pre-trained decoder parameters from the old task can be reused to expedite learning in the new task. However, the existing Iterative Closest Point (ICP) method easily fails as it is sensitive to neural data distribution. This paper proposes a pair-wise Kullback Leibler (KL) divergence optimizing framework for stable neural pattern alignment. The KL divergence measures the difference between the data distribution of the previous task and the aligned new task. The alignment process is formulated as an optimization problem by minimizing the KL divergence. The proposed algorithm is tested in a simulated experiment where a rat learns a two-lever discrimination task from a one-lever pressing task. Three scenarios are designed to test the feasibility of our algorithm, including non-Gaussian neural pattern shapes, noisy neural data, and different alignment angles. The results demonstrate that the proposed method is more robust than ICP, indicating its potential to discover the brain's alignment mechanism more accurately.