Noise-driven temporal association in neural networks

A network of spinlike neurons with asymmetric exchange interactions and stochastic spike response which can learn and recall time sequences of biased patterns is proposed. Noise makes synapses with delayed response or with time-dependent strength, previously proposed for storage of time sequences, superfluous. An accurate timing of pattern sequences requires a sufficient number N of neurons. The performance of the suggested network is described by Monte Carlo simulation, in terms of a Fokker-Planck equation and, for N+ 00, in terms of a Liouville equation.