Pattern formation in a thermosensitive neural network

Abstract

Pattern formation and synchronization stability in the network are dependent on cooperation between nodes and the local kinetics as well. Temperature has a significant effect on the membrane potential by regulating the channel conductance and excitability of neurons. Thermistor is a smart sensor and its resistance value changes with temperature. A nonlinear circuit composed of thermistors is effective to detect the changes in surroundings temperature. In this paper, a temperature-induced pattern formation in a two-dimensional regular lattice network composed of thermosensitive neurons is studied. Numerical simulations are performed to research the effect of parameters (coupling amplitude) and temperature spatial gradient distribution of neural network on pattern formation and synchronization stability. The bifurcation analysis of the membrane potential reveals the conversion in electrical activity and pattern selection. The results show that neural membrane potential can present distinct firing patterns with the change of temperature. The electrical synapse coupling between neurons promotes effective signals propagation in the network. It is found that the collective dynamic behavior of the network is changed by taming the coupling intensity. The Hamilton energy is estimated to predict the mode selection in neural activities. The energy can be ordered in the network under the temperature gradient distribution. Our results confirm that electrical synapse coupling and temperature spatial gradient distribution can be controlled to affect the neural activities in nervous system.