Search for: All records

Some neural representations gradually change across multiple timescales. Here we argue that modeling this “drift” could help explain the spacing effect (the long-term benefit of distributed learning),whereby differences between stored and current temporal context activity patterns produce greater error-driven learning. We trained a neurobiologically realistic model of the entorhinal cortex and hippocampus to learn paired associates alongside temporal context vectors that drifted between learning episodes and/or before final retention intervals. In line with spacing effects, greater drift led to better model recall after longer retention intervals. Dissecting model mechanisms revealed that greater drift increased error-driven learning, strengthened weights in slower drifting temporal context neurons (temporal abstraction), and improved direct cue–target associations (decontextualization). Intriguingly, these results suggest that decontextualization—generally ascribed only to the neocortex—can occur within the hippocampus itself. Altogether, our findings provide a mechanistic formalization for established learning concepts such as spacing effects and errors during learning. 

The planar Turán number $$\textrm{ex}_{\mathcal{P}}(C_{\ell},n)$$ is the largest number of edges in an $$n$$-vertex planar graph with no $$\ell$$-cycle. For each $$\ell\in \{3,4,5,6\}$$, upper bounds on $$\textrm{ex}_{\mathcal{P}}(C_{\ell},n)$$ are known that hold with equality infinitely often. Ghosh, Győri, Martin, Paulos, and Xiao [arXiv:2004.14094] conjectured an upper bound on $$\textrm{ex}_{\mathcal{P}}(C_{\ell},n)$$ for every $$\ell\ge 7$$ and $$n$$ sufficiently large. We disprove this conjecture for every $$\ell\ge 11$$. We also propose two revised versions of the conjecture. 

This paper proposes a novel resilience assessment approach for power system. Two resilience indices are developed from the perspectives of the system and individual component levels, respectively. The former one quantifies the resilience of a power system in a system-wide manner, while the latter is intended to assess the individual component through the pre-disruption and post-disruption indices. Specifically, the pre-disruption index is used to determine the weak points of the system before the occurrence of disruptions, while the post-disruption index is for designing the optimal restoration strategies. We advocate the use of impact-increment-based state enumeration method to calculate the presented indices in an efficient way without loss of accuracy. Numerical results carried out on the IEEE RTS-79 test system and the IEEE 118-bus system validate the effectiveness of the proposed approach and indices.