コンピュータサイエンスとシステム生物学のジャーナル

Computational Investigation of Complexity and Robustness in Neural Circuits

Abstract

Jeffrey E. Arle and Longzhi Mei

The stability of evolutionary systems is critical to their survival and reproductive success. In the face of continuous extrinsic and intrinsic stimuli, biological systems must evolve to perform robustly within sub-regions of parameter and trajectory spaces that are often astronomical in magnitude, characterized as homeostasis over a century ago in medicine and later in cybernetics and control theory. Various evolutionary design strategies for robustness have evolved and are conserved across species, such as redundancy, modularity, and hierarchy. We investigate the hypothesis that a strategy for robustness is in evolving neural circuitry network components and topology such that increasing the number of components results in greater system stability. As measured by a center of maximum curvature method related to firing rates, the transition of the neural circuitry systems model to a robust state was ~153 network connections (network degree).