Generation of context dependent sequences by multiple-timescale neural network through successive bifurcations
Tomoki Kurikawa, Kunihiko Kaneko · 2020
The generation of robust sequential patterns that depend flexibly on the previous history of inputs and outputs is essential to temporal information processing with working memory in our neural system. We propose a neural network with two timescales, in which a sequence of fixed points of the fast dynamics is generated through bifurcations by slow dynamics as a control parameter. By adopting a simple, biologically plausible learning rule, the neural network can recall a complex context-dependent sequence. Considering multiple timescales experimentally observed in cortical areas, this study provides a general scheme to temporal processing in the brain.