  The performance of the Hopfield neural network model is numerically studied
on various complex networks, such as the Watts-Strogatz network, the
Barab{\'a}si-Albert network, and the neuronal network of the C. elegans.
Through the use of a systematic way of controlling the clustering coefficient,
with the degree of each neuron kept unchanged, we find that the networks with
the lower clustering exhibit much better performance. The results are discussed
in the practical viewpoint of application, and the biological implications are
also suggested.
