Recursive self-organizing maps.
This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to SOM, because feedback generates instability during learning. We demonstrate a solution to this problem, based on a nonlinearity. The result is a generalization of SOM that learns to represent sequences recursively. We demonstrate that the resulting representations are adapted to the temporal statistics of the input series.
Voegtlin
Thomas
T
Institut des Sciences Cognitives, CNRS UMR 5015, Bron, France. voegtlin@isc.cnrs.fr
D000465:Algorithms; D016571:Neural Networks, Computer