arXiv:1506.02361
Microscopic approach of a time elapsed neural model
원문 초록
The spike trains are the main components of the information processing in the brain. To model spike trains several point processes have been investigated in the literature. And more macroscopic approaches have also been studied, using partial differential equation models. The main aim of the present article is to build a bridge between several point processes models (Poisson, Wold, Hawkes) that have been proved to statistically fit real spike trains data and age-structured partial differential equations as introduced by Pakdaman, Perthame and Salort.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Differentiable Nonlinear Model Predictive Control
Making Language Model a Hierarchical Classifier and Generator
Implementing Adaptations for Vision AutoRegressive Model
Graph World Model
Compress Any Segment Anything Model (SAM)
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval
토론
이 논문과 연결된 포럼 글이 아직 없습니다.