Computational modeling of neural plasticity for self-organization of neural networks

Joseph Chrol-Cannon, Yaochu Jin
2014 Biosystems (Amsterdam. Print)  
Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spiketiming activity in connected neurons. In the eld of computational neuroscience, much eort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural
more » ... zation, as well as its inuence on the learning performance of neural networks for accomplishing machine learning tasks such as classication and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines ndings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence.
doi:10.1016/j.biosystems.2014.04.003 pmid:24769242 fatcat:mqkncfb2ordzrokovnfspitgym