Yi Zeng — Page 12 | Searcharxiv

arXiv2013

Synchrony and Periodicity in an Excitable Stochastic Neural Network with Multiple Subpopulations

We consider a fully stochastic excitatory neuronal network with a number of subpopulations with different firing rates. We show that as network size goes to infinity, this limits on a deterministic hybrid model whose trajectories are discontinuous. The jumps in the limit correspond to large synchronous events that involve a large proportion of the network. We also perform a rigorous analysis of the limiting deterministic system in certain cases, and show that it displays synchrony and periodicity in a large region of parameter space.

math.DS↗

arXiv2008

On Granular Knowledge Structures

Knowledge plays a central role in human and artificial intelligence. One of the key characteristics of knowledge is its structured organization. Knowledge can be and should be presented in multiple levels and multiple views to meet people's needs in different levels of granularities and from different perspectives. In this paper, we stand on the view point of granular computing and provide our understanding on multi-level and multi-view of knowledge through granular knowledge structures (GKS). Representation of granular knowledge structures, operations for building granular knowledge structures and how to use them are investigated. As an illustration, we provide some examples through results from an analysis of proceeding papers. Results show that granular knowledge structures could help users get better understanding of the knowledge source from set theoretical, logical and visual point of views. One may consider using them to meet specific needs or solve certain kinds of problems.

cs.AI↗