This paper proposes an improved add-if-silent rule, which is suited for training intermediate layers of a multi-layered convolutional network, such as a neocognitron. By the add-if-silent rule, a new cell is generated if all postsynaptic cells are silent. The generated cell learns the activity of the presynaptic cells in one-shot, and its input connections will never be modified afterward. To use this learning rule for a convolutional network, it is required to decide at which retinotopic location this rule is to be applied. In the conventional add-if-silent rule, we chose the location where the activity of presynaptic cells is the largest. In the proposed new learning rule, a negative feedback is introduced from postsynaptic cells to presynaptic cells, and a new cell is generated at the location where the presynaptic activity fails to be suppressed by the feedback. We apply this learning rule to a neocognitron for hand-written digit recognition, and demonstrate the decrease in the recognition error. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Fukushima, K. (2014). One-shot learning with feedback for multi-layered convolutional network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 291–298). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_37
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