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arXiv · 1906.11770

Repeated sequential learning increases memory capacity via effective decorrelation in a recurrent neural network

Tomoki Kurikawa·Omri Barak·Kunihiko Kaneko
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Abstract

Memories in neural system are shaped through the interplay of neural and learning dynamics under external inputs. By introducing a simple local learning rule to a neural network, we found that the memory capacity is drastically increased by sequentially repeating the learning steps of input-output mappings. The origin of this enhancement is attributed to the generation of a Psuedo-inverse correlation in the connectivity. This is associated with the emergence of spontaneous activity that intermittently exhibits neural patterns corresponding to embedded memories. Stablization of memories is achieved by a distinct bifurcation from the spontaneous activity under the application of each input.

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Tomoki Kurikawa, Omri Barak, Kunihiko Kaneko. 2019-06-22. Repeated sequential learning increases memory capacity via effective decorrelation in a recurrent neural network. https://doi.org/10.1103/physrevresearch.2.023307

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