activity
19992005
most citedChaos synchronization in gap-junction-coupled neurons

41 citations · 106 across the 4 of their papers we have counts for

collaborators

6 papers

nlin.CD2005★ 41 cited

Chaos synchronization in gap-junction-coupled neurons

Masahiko Yoshioka

Depending on temperature the modified Hodgkin-Huxley (MHH) equations exhibit a variety of dynamical behavior including intrinsic chaotic firing. We analyze synchronization in a lar…

nlin.PS2005★ 34 cited

Cluster synchronization in an ensemble of neurons interacting through chemical synapses

Masahiko Yoshioka

In networks of periodically firing spiking neurons that are interconnected with chemical synapses, we analyze cluster state, where an ensemble of neurons are subdivided into a few…

cond-mat.dis-nn2002★ 7 cited

Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons

Masahiko Yoshioka

We study associative memory neural networks of the Hodgkin-Huxley type of spiking neurons in which multiple periodic spatio-temporal patterns of spike timing are memorized as limit…

cond-mat.dis-nn2001★ 24 cited

The spike-timing-dependent learning rule to encode spatiotemporal patterns in a network of spiking neurons

Masahiko Yoshioka

We study associative memory neural networks based on the Hodgkin-Huxley type of spiking neurons. We introduce the spike-timing-dependent learning rule, in which the time window wit…

cond-mat.dis-nn1999

Associative memory storing an extensive number of patterns based on a network of oscillators with distributed natural frequencies in the presence of external white noise

Masahiko Yoshioka, Masatoshi Shiino

We study associative memory based on temporal coding in which successful retrieval is realized as an entrainment in a network of simple phase oscillators with distributed natural f…

cond-mat.dis-nn1999

Oscillator neural network model with distributed native frequencies

Michiko Yamana, Masatoshi Shiino, Masahiko Yoshioka

We study associative memory of an oscillator neural network with distributed native frequencies. The model is based on the use of the Hebb learning rule with random patterns ($ξ_i^…