10
Effects of Network Topology on Decision-making Behavior in a Biological Network Model
Authors: Suojun Lu, Jian'an Fang, Qingying Miao
Number of views: 359
The topology of a network often plays a crucial role in determining its dynamical features. There is
increasing interest in trying to understand the relationship between the structural properties of networks and their
behaviors. However, this problem has not been fully considered in many neural networks, which mimic certain
biological processes. Here, we construct a recurrent network model to simulate the decision-making process of brain.
In this model, we examined the effects of network topology on the performance of decision-making by constructing
three different topological networks: the regular network, the random network, and the small-world network. We
found that the regular network and the small-world network show significant better performance of decision-making
than that in the random network when the internal noise of the networks is low. However, following the increase of
the internal noise, the random network, instead of two other networks, shows better ability to resist the noise. Finally,
to mimic neurodegeneration or neural injury, we introduced two types of neuronal damages: clustered damage and
distributed damage. We found that three networks exhibit different network behaviors in the case of neuronal
damages. The regular network and the small-world network display severe decrease of the performance in the
distributed damage pattern, but not in the clustered damage pattern. The random network shows similarly gradual
decrease of the performance in both damage patterns. Furthermore, the small-world network shows the best
performance in the high levels of distributed damage. Together, our results indicate that network topology
significantly influence the network behaviors in our model of decision-making.