Learning Shapes Bifurcations of Neural Dynamics upon External Stimuli. - Tomoki Kurikawa; Kunihiko Kaneko (ICONIP (1), 2010)

Learning to memorize input-output mapping as bifurcation in neural dynamics: relevance of multiple timescales for synapse changes. - Tomoki Kurikawa; Kunihiko Kaneko (Neural Comput. Appl., 2012)

Embedding Responses in Spontaneous Neural Activity Shaped through Sequential Learning. - Tomoki Kurikawa; Kunihiko Kaneko (PLoS Comput. Biol., 2013)

Memories as bifurcations: Realization by collective dynamics of spiking neurons under stochastic inputs. - Tomoki Kurikawa; Kunihiko Kaneko (Neural Networks, 2015)

Repeated sequential learning increases memory capacity via effective decorrelation in a recurrent neural network. - Tomoki Kurikawa; Omri Barak; Kunihiko Kaneko (CoRR, 2019)

Transitions Among Metastable States Underlie Context-Dependent Working Memories in a Multiple Timescale Network. - Tomoki Kurikawa (ICANN (1), 2021)

Intermediate Sensitivity of Neural Activities Induces the Optimal Learning Speed in a Multiple-Timescale Neural Activity Model. - Tomoki Kurikawa (ICONIP (6), 2021)

Multiple-Timescale Neural Networks: Generation of History-Dependent Sequences and Inference Through Autonomous Bifurcations. - Tomoki Kurikawa; Kunihiko Kaneko (Frontiers Comput. Neurosci., 2021)

Optimal Excitatory and Inhibitory Balance for High Learning Performance in Spiking Neural Networks with Long-Tailed Synaptic Weight Distributions. - Ibuki Matsumoto; Sou Nobukawa; Tomoki Kurikawa; Nobuhiko Wagatsuma; Yusuke Sakemi; Takashi Kanamaru; Nina Sviridova; Kazuyuki Aihara (IJCNN, 2023)