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Katsunori Kitano
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2010 – 2019
- 2019
- [c8]Song Ye, Keiichi Kitajo, Katsunori Kitano
:
Detection of Directional Information Flow Induced by TMS Based on Symbolic Transfer Entropy. ICANN (1) 2019: 114-118 - 2017
- [j14]Kento Suzuki, Toshio Aoyagi
Bayesian Estimation of Phase Dynamics Based on Partially Sampled Spikes Generated by Realistic Model Neurons. Frontiers Comput. Neurosci. 11: 116 (2017) - [c7]Nobuhiro Hinakawa, Katsunori Kitano:
Robust and Adaptable Motor Command Representation with Sparse Coding. ICANN (1) 2017: 155-162 - 2016
- [j13]Ryota Kobayashi
Impact of slow K+ currents on spike generation can be described by an adaptive threshold model. J. Comput. Neurosci. 40(3): 347-362 (2016) - [c6]Nobuhiro Hinakawa, Katsunori Kitano:
Effect of Pre- and Postsynaptic Firing Patterns on Synaptic Competition. ICANN (1) 2016: 11-18 - [c5]Kanako Taniguchi, Chieko Koike, Katsunori Kitano:
A Potential Mechanism for Spontaneous Oscillatory Activity in the Degenerative Mouse Retina. ICANN (1) 2016: 63-71 - 2015
- [j12]Daiki Futagi, Katsunori Kitano
Ryanodine-receptor-driven intracellular calcium dynamics underlying spatial association of synaptic plasticity. J. Comput. Neurosci. 39(3): 329-347 (2015) - 2014
- [c4]Hana Ito, Katsunori Kitano:
Pre- and Postsynaptic Properties Regulate Synaptic Competition through Spike-Timing-Dependent Plasticity. ICANN 2014: 733-740 - 2013
- [j11]Ryota Kobayashi
Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model. J. Comput. Neurosci. 35(1): 109-124 (2013) - 2012
- [j10]Tomohiro Fujita, Tomoki Fukai
Influences of membrane properties on phase response curve and synchronization stability in a model globus pallidus neuron. J. Comput. Neurosci. 32(3): 539-553 (2012) - [c3]Daiki Futagi, Katsunori Kitano:
A Biologically Realizable Bayesian Computation in a Cortical Neural Network. ICANN (1) 2012: 247-254
2000 – 2009
- 2008
- [j9]Siu Kang, Katsunori Kitano
Structure of Spontaneous UP and DOWN Transitions Self-Organizing in a Cortical Network Model. PLoS Comput. Biol. 4(3) (2008) - [c2]Katsunori Kitano, Kazuhiro Yamada:
Functional Networks Based on Pairwise Spike Synchrony Can Capture Topologies of Synaptic Connectivity in a Local Cortical Network Model. ICONIP (1) 2008: 978-985 - 2007
- [j8]Katsunori Kitano
Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies. J. Comput. Neurosci. 23(2): 237-250 (2007) - [c1]Hideyuki Câteau, Katsunori Kitano, Tomoki Fukai:
Interactions between Spike-Timing-Dependent Plasticity and Phase Response Curve Lead to Wireless Clustering. ICONIP (1) 2007: 142-150 - 2004
- [j7]Siu Kang, Katsunori Kitano
Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons. Neural Networks 17(3): 307-312 (2004) - 2003
- [j6]Katsunori Kitano
Time representing cortical activities: two models inspired by prefrontal persistent activity. Biol. Cybern. 88(5): 387-394 (2003) - 2002
- [j5]Tomoki Fukai
Modeling the layer V cortical pyramidal neurons showing theta-rhythmic firing in the presence of muscarine. Neurocomputing 44-46: 103-108 (2002) - [j4]Hideyuki Câteau
An accurate and widely applicable method to determine the distribution of synaptic strengths formed by the spike-timing-dependent learning. Neurocomputing 44-46: 343-351 (2002) - [j3]Katsunori Kitano, Hideyuki Câteau
Sustained activity with low firing rate in a recurrent network regulated by spike-timing-dependent plasticity. Neurocomputing 44-46: 473-478 (2002) - 2001
- [j2]Katsunori Kitano, Toshio Aoyagi
Synchronous and asynchronous activities in a network model of the striatal spiny projection neurons. Neurocomputing 38-40: 721-726 (2001)
1990 – 1999
- 1998
- [j1]Toshio Aoyagi
Retrieval Dynamics in Oscillator Neural Networks. Neural Comput. 10(6): 1527-1546 (1998)
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