  Recently, the influence of leakage delay on the dynamics of integer-order
neural networks has been investigated extensively. It has been confirmed that
fractional calculus can depict the memory and hereditary attributes of neural
networks more accurately. In this paper, we study the existence of Hopf
bifurcation in a six-neuron fractional bidirectional associative memory (BAM)
neural network with leakage delay. By selecting two appropriate bifurcation
parameters and analyzing corresponding characteristic equations, it is verified
that the delayed fractional neural network generates a Hopf bifurcation when
the bifurcation parameters pass through some critical values. In order to
measure how much impact of leakage delay on Hopf bifurcation, sensitivity
analysis methods, such as scatter plots and partial rank correlation
coefficients (PRCCs), are introduced to assess the sensitivity of bifurcation
amplitudes to leakage delay. Numerical examples are carried out to illustrate
the theoretical results and help us gain an insight into the effect of leakage
delay vividly.
