DYNAMICS OF NEURAL NETWORKS RELEVANT TO PROPERTIES OF PROTEINS

We studied how the dynamics of Hopfield neural networks depend on comp utational and physical properties of the network. The dynamics of the network was characterized by the distribution of first passage times ( FPT) between the states. The FPT distributions depended on the updatin g scheme, temperature, connectivity range, and number of stored memori es. The FTP distributions were different for synchronous and asynchron ous updating, and were more physically consistent for the synchronous than for the asynchronous updating scheme. Neural networks and protein s share common features such as many degrees of freedom, conflicting c onstraints on energy minimization, and energy functions with many loca l minima. Thus the general lessons learned here on how the dynamics of neural networks depends on their physical properties may be relevant in understanding how the dynamics of proteins is influenced by similar physical properties.