This paper is devoted to the study of the behavior of a specific but large class of linear boolean nets . The class is obtained by fixing the synaptic matrix A, which connects the n neurons, in such a way that a specific set of n patterns evolves linearly. Our main result consists of the establishment of connections between the evolution of the n patterns of the specific set and the evolution of the complete set of 2n states. In particular, the stable states of the network and their attraction basins can be obtained from knowledge of the stable states of the specific set. In addition, we give a rule to store these states, and to obtain an Associative Memory in computational time (polynomial in n).
The Behaviour and Learning of a Deterministic Neural Net
TAGLIAFERRI, Roberto
1992-01-01
Abstract
This paper is devoted to the study of the behavior of a specific but large class of linear boolean nets . The class is obtained by fixing the synaptic matrix A, which connects the n neurons, in such a way that a specific set of n patterns evolves linearly. Our main result consists of the establishment of connections between the evolution of the n patterns of the specific set and the evolution of the complete set of 2n states. In particular, the stable states of the network and their attraction basins can be obtained from knowledge of the stable states of the specific set. In addition, we give a rule to store these states, and to obtain an Associative Memory in computational time (polynomial in n).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
