Increasing the Biological Inspiration of Neural Networks

Authors:
Francesco E. Lauria
Affiliations:
-
Venue:
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Year:
2002

Citing 2
Cited 0

Fundamentals of Artificial Neural Networks

Fundamentals of Artificial Neural Networks
Storage and Recall of Complex Temporal Sequences through a Contextually Guided Self-Organizing Neural Network

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3

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Abstract

Starting from a nerve cell functional characterization, we define formally the autonomous learning to concatenate sequences and prove it to be a possible solution for the problem that faces the, eg vertebrate, nervous systems: ie, to choose and to store, without outside help, the instructions to compute the actual sensor/effector correspondences they have to control. In our formal system we assign the initial connection matrix elements so that the rules, namely the Caianiello relation iterated application, autonomously and deterministically control the meta-rule, namely the Hebbian rule, application.