Threshold Noise as a Source of Volatility in Random Synchronous Asymmetric Neural Networks
Henrik Georg Bohr, Patrick Charles McGuire, Chris Pershing, Johann Rafelski A · arXiv (Cornell University) · 1997
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.