  We study the diversity of complex spatio-temporal patterns of random
synchronous asymmetric neural networks (RSANNs). Specifically, we investigate
the impact of noisy thresholds on network performance and find that there is a
narrow and interesting region of noise parameters where RSANNs display specific
features of behavior desired for rapidly `thinking' systems: accessibility to a
large set of distinct, complex patterns.
