Effect of propagation noise on the network dynamics of a flux coupled conductance-based neuron model

We show the dynamical behavior of a flux coupled (memristive) conductance-based neuron (FCN) by exposing it to an external periodic stimulus. To do so, we first examine the local behavior of isolated FCN using bifurcation analysis, and we discover that the cascading bifurcation transitions between the periodic and chaotic attractors via period-doubling and inverse period-doubling routes. The observed attractors transitions are also supported by Lyapunov exponents. In addition, we check the antimonotonicity phenomenon using bifurcation analysis when tuning the amplitude of the external forcing current. Followed by the collective dynamical behaviors of FCN are explored by extending to the network of neurons under the influence of propagation noise. In the absence of noise, the transition from desynchronized to synchronized state is observed via chimera and cluster states. Similar dynamical transitions are noticed in the presence of propagation noise as well. Interestingly, we discovered that noise may aid in synchronization even at weak coupling strength. Our study will shed light on the emergent dynamics in the presence of additional propagation noise.