activity
19992019
most citedSynapse efficiency diverges due to synaptic pruning following over-growth

21 citations · 31 across the 7 of their papers we have counts for

collaborators

7 papers

cond-mat.dis-nn2019★ 3 cited

Correspondence between Phase Oscillator Network and Classical XY Model with the same random and frustrated interactions

Tomoyuki Kimoto, Tatsuya Uezu

We study correspondence between a phase oscillator network with distributed natural frequencies and a classical XY model at finite temperatures with the same random and frustrated…

cond-mat.stat-mech2014★ 4 cited

Correspondence between phase oscillator network and classical XY model with the same infinite-range interaction in statics

T. Uezu, T. Kimoto, S. Kiyokawa +1

We study the phase oscillator networks with distributed natural frequencies and classical XY models both of which have a class of infinite-range interactions in common. We find tha…

cond-mat.dis-nn2012★ 2 cited

Solvable model of a phase oscillator network on a circle with infinite-range Mexican-hat-type interaction

Tatsuya Uezu, Tomoyuki Kimoto, Masato Okada

We describe a solvable model of a phase oscillator network on a circle with infinite-range Mexican-hat-type interaction. We derive self-consistent equations of the order parameters…

cond-mat.dis-nn2002★ 1 cited

Mixed states in a sparsely encoded associative memory model storing ultrametric patterns

Tomoyuki Kimoto, Masato Okada

When mixed states are composed of s memory patterns, s types of mixed states, which can become equilibrium states of the model, can be generated. We previously reported that storag…

cond-mat.dis-nn2002★ 21 cited

Synapse efficiency diverges due to synaptic pruning following over-growth

Kazushi Mimura, Tomoyuki Kimoto, Masato Okada

In the development of the brain, it is known that synapses are pruned following over-growth. This pruning following over-growth seems to be a universal phenomenon that occurs in al…

cond-mat.dis-nn2002

Mixed states on neural network with structural learning

Tomoyuki Kimoto, Masato Okada

We investigated the properties of mixed states in a sparsely encoded associative memory model with a structural learning method. When mixed states are made of s memory patterns, s…