发现神经解码中位置预测的正态分布规律，构建类高尔顿板算法框架。
An Algorithm Board in Neural Decoding
- 从机器学习与统计学角度分析神经数据分布变化
- 实验证明位置预测遵循正态分布，支持对称性假设
- 构建类高尔顿板算法板，提升系统可解释性
理解神经编码与解码机制一直是神经科学与认知智能领域的研究热点。以往研究在运动场景中发现无监督方法解码的神经数据存在对称性，并基于此构建了认知学习系统。然而，影响神经解码位置的数据流分布状态仍不明确，限制了系统的可解释性提升。本文从机器学习与数学统计视角，探索系统内分布状态的变化。实验中采用多种数学与统计工具评估该对称性的正确性。结果表明，正态分布（或高斯分布）在系统的位置预测解码中起关键作用。最终，构建了一个类似高尔顿板的算法板，作为所发现对称性的数学基础。
原文摘要 · Abstract (English)
Understanding the mechanisms of neural encoding and decoding has always been a highly interesting research topic in fields such as neuroscience and cognitive intelligence. In prior studies, some researchers identified a symmetry in neural data decoded by unsupervised methods in motor scenarios and constructed a cognitive learning system based on this pattern (i.e., symmetry). Nevertheless, the distribution state of the data flow that significantly influences neural decoding positions still remains a mystery within the system, which further restricts the enhancement of the system's interpretability. Based on this, this paper mainly explores changes in the distribution state within the system from the machine learning and mathematical statistics perspectives. In the experiment, we assessed the correctness of this symmetry using various tools and indicators commonly utilized in mathematics and statistics. According to the experimental results, the normal distribution (or Gaussian distribution) plays a crucial role in the decoding of prediction positions within the system. Eventually, an algorithm board similar to the Galton board was built to serve as the mathematical foundation of the discovered symmetry.
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