Department of Psychology, Tsinghua University, Beijing 100084, China and Tsinghua Brain and Intelligence Lab, Beijing 100084, China.
Phys Rev E. 2021 Jan;103(1-1):012406. doi: 10.1103/PhysRevE.103.012406.
While causality processing is an essential cognitive capacity of the neural system, a systematic understanding of the neural coding of causality is still elusive. We propose a physically fundamental analysis of this issue and demonstrate that the neural dynamics encodes the original causality between external events near homomorphically. The causality coding is memory robust for the amount of historical information and features high precision but low recall. This coding process creates a sparser representation for the external causality. Finally, we propose a statistic characterization for the neural coding mapping from the original causality to the coded causality in neural dynamics.
虽然因果关系处理是神经系统的基本认知能力，但对于因果关系的神经编码仍难以捉摸。我们提出了对这个问题的物理基础分析，并证明了神经动力学对外部事件之间的原始因果关系进行了近同态编码。因果编码对于历史信息量具有记忆稳健性，并且具有高精度和低召回率。这个编码过程为外部因果关系创造了一个更稀疏的表示。最后，我们提出了一种统计特征描述，用于描述神经动力学中从原始因果关系到编码因果关系的神经编码映射。
