activity
20192025
most citedRepeated sequential learning increases memory capacity via effective decorrelation in a recurrent neural network

11 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

q-bio.NC2025

Slow and Fast Neurons Cooperate in Contextual Working Memory through Timescale Diversity

Tomoki Kurikawa

Neural systems process information across a broad range of intrinsic timescales, both within and across cortical areas. While such diversity is a hallmark of biological networks, i…

q-bio.NC2025

Stability Control of Metastable States as a Unified Mechanism for Flexible Temporal Modulation in Cognitive Processing

Tomoki Kurikawa, Kunihiko Kaneko

Flexible modulation of temporal dynamics in neural sequences underlies many cognitive processes. For instance, we can adaptively change the speed of motor sequences and speech. Whi…

cond-mat.dis-nn2024

Fluctuation-learning relationship in neural networks

Tomoki Kurikawa, Kunihiko Kaneko

Learning in neural systems occurs through change in synaptic connectivity that is driven by neural activity. Learning performance is influenced by both neural activity and the task…

q-bio.NC2023

Solvable Neural Network Model for Input-Output Associations: Optimal Recall at the Onset of Chaos

Tomoki Kurikawa, Kunihiko Kaneko

In neural information processing, an input modulates neural dynamics to generate a desired output. To unravel the dynamics and underlying neural connectivity enabling such input-ou…

physics.bio-ph2021

Transitions among metastable states underlie context-dependent working memories in a multiple timescale network

Tomoki Kurikawa

Transitions between metastable states are commonly observed in the neural system and underlie various cognitive functions such as working memory. In a previous study, we have devel…

nlin.AO2020

Multiple-timescale Neural Networks: Generation of Context-dependent Sequences and Inference through Autonomous Bifurcations

Tomoki Kurikawa, Kunihiko Kaneko

Sequential transitions between metastable states are ubiquitously observed in the neural system and underlie various cognitive functions. Although a number of studies with asymmetr…

nlin.AO2019★ 11 cited

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

Tomoki Kurikawa, Omri Barak, Kunihiko Kaneko

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, w…