Work

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

Year: 2020

Type: article

Abstract: The authors show that repeated learning decorrelates memory representations, resulting in higher memory performance than other classical models. Such enhancement depends on the relationship between neural dynamics and synaptic plasticity. The model's spontaneous activity is shaped by learning, to exhibit chaotic activity with intermittent correlations to learned memories, as is consistent with experimental observations.

Source: Physical Review Research

DOI: 10.1103/physrevresearch.2.023307

Authors: Tomoki Kurikawa, Omri Barak, Kunihiko Kaneko

Institution: Kansai Medical University

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: Hitachi, Israel Science Foundation, University of Tokyo, Japan Society for the Promotion of Science