High-dimensional vectors have been proposed as a neural method for representing information in the brain using Vector Symbolic Algebras (VSAs). While previous work has explored decoding and cleaning up these vectors under the noise that arises during computation, existing methods are limited. Cleanup methods are essential for robust computation within a VSA. However, cleanup methods for continuous-value encodings are not as effective. In this paper, we present an iterative optimization method to decode and clean up Fourier Holographic Reduced Representation (FHRR) vectors that are encoding continuous values. We combine composite likelihood estimation (CLE) and maximum likelihood estimation (MLE) to ensure convergence to the global optimum. We also demonstrate that this method can effectively decode FHRR vectors under different noise conditions, and show that it outperforms existing methods.

翻译：高维向量作为一种利用向量符号代数（VSA）在神经系统中表示信息的方法已被提出。尽管先前研究已探索在计算过程中产生的噪声下对这些向量进行解码和清理，但现有方法存在局限。清理方法对于VSA框架内的鲁棒计算至关重要，然而针对连续值编码的清理方法效果欠佳。本文提出一种迭代优化方法，用于解码和清理编码连续值的傅里叶全息缩减表示（FHRR）向量。我们结合复合似然估计（CLE）与最大似然估计（MLE）以确保收敛到全局最优解。实验证明该方法能在不同噪声条件下有效解码FHRR向量，且性能优于现有方法。