000002028 001__ 2028
000002028 005__ 20201027123926.0
000002028 022__ $$a2472-1751
000002028 0247_ $$2DOI$$a10.1162/netn_a_00082
000002028 035__ $$a4400
000002028 037__ $$aPUBART
000002028 041__ $$aeng
000002028 245__ $$aOptimal modularity and memory capacity of neural reservoirs
000002028 269__ $$a2019-05-02
000002028 336__ $$aPublished Article
000002028 520__ $$aThe neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network’s architecture and function is still primitive. Here we reveal that a neural network’s modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information-spreading processes can be leveraged to better design neural networks and may shed light on the brain’s modular organization.
000002028 700__ $$aRodriguez, Nathaniel
000002028 700__ $$aIzquierdo, Eduardo J.
000002028 700__ $$aAhn, Yong Yeol
000002028 773__ $$tNetwork Neuroscience
000002028 773__ $$j3
000002028 8560_ $$fyyahn@indiana.edu
000002028 85642 $$hElectronic Resource$$uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497001
000002028 909CO $$ooai:iu.tind.io:2028$$pGLOBAL_SET
000002028 980__ $$aPUBART