Abstract
We investigate how network structure can influence the tendency for a neuronal network to synchronize, or its synchronizability, independent of the dynamical model for each neuron. The synchrony analysis takes advantage of the framework of second order networks, which defines four second order connectivity statistics based on the relative frequency of two-connection network motifs. The analysis identifies two of these statistics, convergent connections, and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections. These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks. Surprisingly, divergent connections, which determine the fraction of shared inputs, do not strongly influence the synchrony. The critical role of chains, rather than divergent connections, in influencing synchrony can be explained by their increasing the effective coupling strength. The decrease of synchrony with convergent connections is primarily due to the resulting heterogeneity in firing rates.
Highlights
The nervous system is highly organized to allow information to flow efficiently (Watts and Strogatz, 1998; Song et al, 2005) but is robust to pathological behaviors such as seizures
Overview We examine the influence of network structure on the synchrony of neuronal networks
We generate networks using the framework of second order networks (SONETs) which allows us to systematically vary second order connectivity statistics, which are the frequency of reciprocal, convergent, divergent, and chain connections shown in Figure 1
Summary
The nervous system is highly organized to allow information to flow efficiently (Watts and Strogatz, 1998; Song et al, 2005) but is robust to pathological behaviors such as seizures. In many diseases strong evidence suggests reorganization of the neuronal connections causing or caused by the disease (Cavazos et al, 1991; Parent et al, 1997; Zeng et al, 2007) may play a role in generating these pathological behaviors. The synchronization of spiking activity in neuronal networks is due to a complex interplay among many factors; individual neuron dynamics, the types of synaptic response, external inputs to the network, as well as the network topology all play a role in determining the level of synchronization. The analysis of small networks of neurons has provided significant insight into the ways in which single neuron dynamics and synaptic response can influence the tendency of the network to synchronize (Ermentrout and Kopell, 1991; Strogatz and Mirollo, 1991). The focus of the present manuscript is to examine what types of network structures can decrease or increase the likelihood of a network to synchronize
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
