Gap structure of the local field in symmetric Q Ising neural networks.
The time evolution of the local field in symmetric Q-Ising neural networks is studied for arbitrary Q. In particular, the structure of the noise and the appearance of gaps in the probability distribution are discussed. Results are presented for several values of Q and compared with numerical simulations.
Instituut voor Theoretische Fysica, Katholieke Universiteit Leuven, B-3001 Leuven, Belgium. desire.bolle@fys.kuleuven.ac.be
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