An ingenious scheme to bifurcations in a fractional-order Cohen–Grossberg neural network with different delays

It detects that fractional calculus incorporating into neural networks can neatly reflect the memory properties of neurons. Therefore, we focus on the dynamic bifurcations of a fractional-order Cohen–Grossberg neural network with diverse delays in this paper. Firstly, we use Cramer’s rule to analyze the characteristic equation containing fourth-order transcendental terms and study the stability and Hopf bifurcation of the system. The acquired results show that communication and leakage delays significantly affect the time-delayed fractional-order neural network stability. Secondly, we numerically affirm that different fractional orders affect Hopf bifurcation eminently. The last two examples exhibit the validity of the theoretical results.