The Physics of Living Neural Networks

拓扑堆积统计区分生命物质与非生命物质

Topological packing statistics distinguish living and non-living matter

研究提出一种测量3D点云拓扑距离的数学框架，通过Delaunay细分的统计特性区分生命与非生命系统，构建了涵盖多种生物和非生物结构的通用拓扑图谱，实现了对3D无序介质的统一分类。

活细胞能否利用相变处理信息？

Could Living Cells Use Phase Transitions to Process Information?

该研究探讨细胞内生物分子凝聚这一结构相变的信息处理作用，将其纳入物理计算框架，提出表达性与学习性问题，为多学科研究提供新方向。

Tailored ensembles of neural networks optimize sensitivity to stimulus statistics

The dynamic range of stimulus processing in living organisms is much larger than a single neural network can explain. For a generic, tunable spiking network we derive that while the dynamic range is maximal at criticality, the interval of discriminable intensities is very similar for any network tuning due to coalescence. Compensating coalescence enables adaptation of discriminable intervals. Thus, we can tailor an ensemble of networks optimized to the distribution of stimulus intensities, e.g., extending the dynamic range arbitrarily. We discuss potential applications in machine learning.

生命系统的对称性：生物网络中的对称纤维化与同步化

Symmetries of Living Systems: Symmetry Fibrations and Synchronization in Biological Networks

本书受范畴论格罗滕迪克纤维化启发，提出局域灵活的对称纤维化概念，为生物网络提供新几何框架，解释多生物域结构-功能关系并支持网络降维与同步功能。

Bidirectional communication in neural networks moderated by a Hebb-like learning rule

We demonstrate that our recently introduced stochastic Hebb-like learning rule is capable of learning the problem of timing in general network topologies generated by an algorithm of Watts and Strogatz. We compare our results with a learning rule proposed by Bak and Chialvo and obtain not only a significantly better convergence behavior but also a dependence of the presentation order of the patterns to be learned by introduction of an additional degree of freedom which allows the neural network to select the next pattern itself whereas the learning rule of Bak and Chialvo stays uneffected. Thi…