Control of dynamical states in a network: firing death and multistability

We show that the chaotically spiking neurons coupled in a ring configuration changes their internal dynamics to subthreshold oscillations, the phenomenon referred to as firing death. These dynamical changes are observed below the critical coupling strength at which the transition to full chaotic synchronization occurs. We find various dynamical regimes in the subthreshold oscillations, namely, regular, quasi-periodic and chaotic states. We show numerically that these dynamical states may coexist with large amplitude spiking regimes and that this coexistence is characterized by riddled basins of attraction. Moreover, we show that under a particular coupling configuration, the neural network exhibits bistability between two configurations of clusters. Each cluster composed of two neurons undergoes independent chaotic spiking dynamics. As an appropriate external perturbation is applied to the system, the network undergoes changes in the clusters configuration, involving different neurons at each time. We hypothesize that the winning cluster of neurons, responsible for perception, is that exhibiting higher mean frequency. The clusters features may contribute to an increase of local field potential in the neural network. The reported results are obtained for neurons implemented in the electronic circuits as well as for the model equations.