  Many experiments have evidenced that electrical and chemical synapses --
hybrid synapses -- coexist in most organisms and brain structures. The role of
electrical and chemical synapse connection in diversity of neural activity
generation has been investigated separately in networks of varying
complexities. Nevertheless, theoretical understanding of hybrid synapses in
diverse dynamical states of neural networks for self-organization and
robustness still has not been fully studied. Here, we present a model of neural
network built with hybrid synapses to investigate the emergence of global and
collective dynamics states. This neural networks consists of excitatory and
inhibitory population interacting together. The excitatory population is
connected by excitatory synapses in small world topology and its adjacent
neurons are also connected by gap junctions. The inhibitory population is only
connected by chemical inhibitory synapses with all-to-all interaction. Our
numerical simulations show that in the balanced networks with absence of
electrical coupling, the synchrony states generated by this architecture are
mainly controlled by heterogeneity among neurons and the balance of its
excitatory and inhibitory inputs. In balanced networks with strong electrical
coupling, several dynamical states arise from different combinations of
excitatory and inhibitory weights. More importantly, we find that these states,
such as synchronous firing, cluster synchrony, and various ripples events,
emerge by slight modification of chemical coupling weights. For large enough
electrical synapse coupling, the whole neural networks become synchronized. Our
results pave a way in the study of the dynamical mechanisms and computational
significance of the contribution of mixed synapse in the neural functions.
