| A new look at state-space models for neural data L Paninski, Y Ahmadian, DG Ferreira, S Koyama, K Rahnama Rad, ... Journal of computational neuroscience 29, 107-126, 2010 | 251 | 2010 |
| Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control S Koyama, SM Chase, AS Whitford, M Velliste, AB Schwartz, RE Kass Journal of computational neuroscience 29, 73-87, 2010 | 180 | 2010 |
| A measure of local variation of inter-spike intervals S Shinomoto, K Miura, S Koyama Biosystems 79 (1-3), 67-72, 2005 | 89 | 2005 |
| Approximate methods for state-space models S Koyama, L Castellanos Pérez-Bolde, CR Shalizi, RE Kass Journal of the American Statistical Association 105 (489), 170-180, 2010 | 81 | 2010 |
| Macroscopic quantum information processing using spin coherent states T Byrnes, D Rosseau, M Khosla, A Pyrkov, A Thomasen, T Mukai, ... Optics Communications 337, 102-109, 2015 | 71 | 2015 |
| Bayesian decoding of neural spike trains S Koyama, UT Eden, EN Brown, RE Kass Annals of the Institute of Statistical Mathematics 62, 37-59, 2010 | 66 | 2010 |
| Remote reprogramming of hepatic circadian transcriptome by breast cancer H Hojo, S Enya, M Arai, Y Suzuki, T Nojiri, K Kangawa, S Koyama, ... Oncotarget 8 (21), 34128, 2017 | 58 | 2017 |
| Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models S Koyama, L Paninski Journal of computational neuroscience 29, 89-105, 2010 | 57 | 2010 |
| Estimating the time-varying reproduction number of COVID-19 with a state-space method S Koyama, T Horie, S Shinomoto PLoS computational biology 17 (1), e1008679, 2021 | 49 | 2021 |
| Empirical Bayes interpretations of random point events S Koyama, S Shinomoto Journal of Physics A: Mathematical and General 38 (29), L531, 2005 | 47 | 2005 |
| Histogram bin width selection for time-dependent Poisson processes S Koyama, S Shinomoto Journal of Physics A: Mathematical and General 37 (29), 7255, 2004 | 39 | 2004 |
| Rabies virus‐mediated oligodendrocyte labeling reveals a single oligodendrocyte myelinates axons from distinct brain regions Y Osanai, T Shimizu, T Mori, Y Yoshimura, N Hatanaka, A Nambu, ... Glia 65 (1), 93-105, 2017 | 38 | 2017 |
| Spike train probability models for stimulus-driven leaky integrate-and-fire neurons S Koyama, RE Kass Neural computation 20 (7), 1776-1795, 2008 | 35 | 2008 |
| A characterization of the time-rescaled gamma process as a model for spike trains T Shimokawa, S Koyama, S Shinomoto Journal of computational neuroscience 29, 183-191, 2010 | 29 | 2010 |
| Neural networks using two-component Bose-Einstein condensates T Byrnes, S Koyama, K Yan, Y Yamamoto Scientific reports 3 (1), 2531, 2013 | 26 | 2013 |
| Phase transitions in the estimation of event rate: A path integral analysis S Koyama, T Shimokawa, S Shinomoto Journal of Physics A: Mathematical and Theoretical 40 (20), F383, 2007 | 20 | 2007 |
| The effect of interspike interval statistics on the information gainunder the rate coding hypothesis S Koyama, L Kostal Mathematical Biosciences & Engineering 11 (1), 63-80, 2013 | 18 | 2013 |
| Identifying exogenous and endogenous activity in social media K Fujita, A Medvedev, S Koyama, R Lambiotte, S Shinomoto Physical Review E 98 (5), 052304, 2018 | 16 | 2018 |
| Length of myelin internodes of individual oligodendrocytes is controlled by microenvironment influenced by normal and input‐deprived axonal activities in sensory deprived mouse … Y Osanai, T Shimizu, T Mori, N Hatanaka, Y Kimori, K Kobayashi, ... Glia 66 (11), 2514-2525, 2018 | 15 | 2018 |
| On the spike train variability characterized by variance-to-mean power relationship S Koyama Neural computation 27 (7), 1530-1548, 2015 | 14 | 2015 |
