title: Impact of network topology on decision-making.
abstract: The dynamical behaviors of a neural system are strongly influenced by its network structure. The present study investigated how the network structure influences decision-making behaviors in the brain. We considered a recurrent network model with four different topologies, namely, regular, random, small-world and scale-free. We found that the small-world network has the best performance in decision-making for low noise, whereas the random network is most robust when noise is strong. The four networks also exhibit different behaviors in the case of neuronal damage. The performances of the regular and the small-world networks are severely degraded in distributed damage, but not in clustered damage. The random and the scale-free networks are, on the other hand, quite robust to both types of damage. Furthermore, the small-world network has the best performance in strong distributed damage.
authors.affiliation: College of Information Science & Technology, Donghua University, 2999 North Renmin Road, Songjiang District, Shanghai 201620, China.
authors.forename: Suojun
authors.initials: S
authors.lastname: Lu
authors.forename: Jian'an
authors.initials: J
authors.lastname: Fang
authors.forename: Aike
authors.initials: A
authors.lastname: Guo
authors.forename: Yueqing
authors.initials: Y
authors.lastname: Peng
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D002540:Cerebral Cortex / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D011930:Reaction Time / Q000502:physiology