Abstract
The dynamic binary neural network is characterized by ternary connection parameters and can generate various binary periodic orbits. This paper considers two interesting problems based on typical examples. First, effect of connection sparsity on stability of target periodic orbits is considered: adding branches adequately to the most sparse network, stability of the periodic orbits can be reinforced. Second, fault tolerance of the network is considered: cutting one branch from the network, storage and stability of the periodic orbits are preserved in high probability.
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