Long-Term Behavior of Neural Networks

摘要: Two important interdisciplinary goals to which statistical physics can potentially make vital contributions are (a) the formulation of dynamical models capture primary features information processing and adaptive behavior in living nervous systems (b) design computational algorithms or devices solve cognitive problems according principles natural intelligence. Currently, both being actively pursued terms neural networks.l–10 A network consists a collection neuron-like units, with synapse-like couplings may be adjusted some learning rule, so as achieve desired performance network. The equation motion determining state at each time is nonlinear dissipative. Generally, long-term such networks considered determine their usefulness artificial contexts: it generally identified response organism given initial stimulus, else solution provided by algorithm device. essential idea exemplified an idealized form content-addressable memory (CAM) discussed Hopfield, based on fully symmetrically connected nets binary neurons governed asynchronous threshold dynamics.11 Started from any state, system evolves necessarily stable fixed-point configuration. end point evolution stored stimulus represented associated. Such distributed, sense that about widely distributed over many synapses (couplings) system; error correcting few errors input will not disturb accurate recall relevant memory.