Nonlinear Sciences – Adaptation and Self-Organizing Systems
Scientific paper
2002-09-04
Phys. Rev. E 68 (2003) 011908
Nonlinear Sciences
Adaptation and Self-Organizing Systems
13 pages, 14 figures, completely revised and augmented version
Scientific paper
10.1103/PhysRevE.68.011908
We suggest a mechanism based on spike time dependent plasticity (STDP) of synapses to store, retrieve and predict temporal sequences. The mechanism is demonstrated in a model system of simplified integrate-and-fire type neurons densely connected by STDP synapses. All synapses are modified according to the so-called normal STDP rule observed in various real biological synapses. After conditioning through repeated input of a limited number of of temporal sequences the system is able to complete the temporal sequence upon receiving the input of a fraction of them. This is an example of effective unsupervised learning in an biologically realistic system. We investigate the dependence of learning success on entrainment time, system size and presence of noise. Possible applications include learning of motor sequences, recognition and prediction of temporal sensory information in the visual as well as the auditory system and late processing in the olfactory system of insects.
Abarbanel Henry D. I.
Nowotny Thomas
Rabinovich Misha I.
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