ORCiD, WoS, GoogleScholar

  1. K. Yoshida and T. Toyoizumi, PNAS Nexus 2, 1-13 (2023). DOI:10.1093/pnasnexus/pgac286   PDF
    Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep.
  2. Z. He and T. Toyoizumi, Neural Computation 35, 38-57 (2023). DOI:10.1162/neco_a_01542   PDF
    Progressive interpretation synthesis: interpreting task solving by quantifying previously used and unused information
  3. T. Toyoizumi, Proc. Natl. Acad. Sci. USA 119:48, e2216092119 (2022). DOI:10.1073/pnas.2216092119
    Ordering in heterogeneous connectome weights for visual information processing
  4. A. Marzoll, K. Shibata, T. Toyoizumi, I. Chavva, T. Watanabe, iScience 25, 105492 (2022). DOI:10.1016/j.isci.2022.105492   PDF
    Decrease in signal-related activity by visual training and repetitive visual stimulation
  5. F. Fumarola, Z. He, Ł. Kuśmierz, and T. Toyoizumi, Physical Review Research 4, 033089 (2022). DOI:10.1103/PhysRevResearch.4.033089   PDF
    Decoding silence in free recall
  6. G. Shimizu, K. Yoshida, H. Kasai, and T. Toyoizumi, Current Opinion in Neurobiology 70, 34-42 (2021). DOI:10.1016/j.conb.2021.06.002   PDF
    Computational roles of intrinsic synaptic dynamics
  7. H. Kasai, N. E. Ziv, H. Okazaki, S. Yagishita, and T. Toyoizumi, Nature Reviews Neuroscience 22, 407-422 (2021). DOI:10.1038/s41583-021-00467-3   PDF
    Spine dynamics in the brain, mental disorders and artificial neural networks
  8. T. Isomura and T. Toyoizumi, Nature Machine Intelligence 3, 434-446 (2021). DOI:10.1038/s42256-021-00306-1   PDF
    Dimensionality reduction to maximize prediction generalization capability
  9. Ł. Kuśmierz and T. Toyoizumi, Proc. Natl. Acad. Sci. USA 118:10, e2024297118 (2021). DOI:10.1073/pnas.2024297118   PDF
    Infection curves on small-world networks are linear only in the vicinity of the critical point
  10. Y. Ito and T. Toyoizumi, PLOS Computational Biology 17, e1008700 (2021). DOI:10.1371/journal.pcbi.1008700   PDF
    Learning poly-synaptic paths with traveling waves
  11. T. Isomura and T. Toyoizumi, Neural Computation 33, 1433-1468 (2021). DOI:10.1162/neco_a_01378   PDF
    On the achievability of blind source separation for high-dimensional nonlinear source mixtures
  12. Ł. Kuśmierz, S. Ogawa, and T. Toyoizumi, Physical Review Letters 125, 028101 (2020). DOI:10.1103/PhysRevLett.125.028101   PDF   Supplemental material
    Edge of chaos and avalanches in neural networks with heavy-tailed synaptic weight distribution
  13. R. Legaspi and T. Toyoizumi, Nature Communications 10:4250 (2019). DOI:10.1038/s41467-019-12170-0   PDF
    A Bayesian psychophysics model of sense of agency
  14. Ł. Kuśmierz and T. Toyoizumi, Physical Review E 100, 032110 (2019). DOI:10.1103/PhysRevE.100.032110   PDF
    Robust random search with scale-free stochastic resetting
  15. J. Humble, K. Hiratsuka, H. Kasai, and T. Toyoizumi, Frontiers in Computational Neuroscience 13:38 (2019) DOI:10.3389/fncom.2019.00038   PDF
    Intrinsic Spine Dynamics Are Critical for Recurrent Network Learning in Models With and Without Autism Spectrum Disorder.
  16. T. Isomura and T. Toyoizumi, Scientific Reports 9:7127 (2019). DOI:10.1038/s41598-019-43423-z  PDF
    Multi-context blind source separation by error-gated Hebbian rule
  17. R. Legaspi, Z. He and T. Toyoizumi, Current Opinion in Behavioral Sciences 29:84-90 (2019). DOI:10.1016/j.cobeha.2019.04.004   PDF
    Synthetic Agency: Sense of Agency in Artificial Intelligence
  18. E. Munro Krull, S. Sakata and T. Toyoizumi, Frontiers in Neuroscience 13:316 (2019). DOI:10.3389/fnins.2019.00316  PDF
    Theta oscillations alternate with high amplitude neocortical population within synchronized states.
  19. H. Okazaki, A. Hayashi-Takagi, A. Nagaoka, M. Negishi, H. Ucar, S. Yagishita, K. Ishii, T. Toyoizumi, K. Fox, and H. Kasai, Neuroscience Letters 671, 99-102 (2018). DOI:10.1016/j.neulet.2018.02.006  PDF
    Calcineurin knockout mice show a selective loss of small spines.
  20. C. L. Buckley and T. Toyoizumi, PLOS Computational Biology 14, e1005926 (2018). DOI:10.1371/journal.pcbi.1005926  PDF  Supporting information
    A theory of how active behavior stabilizes neural activity: neural gain modulation by closed-loop environmental feedback
  21. T. Isomura and T. Toyoizumi, Scientific Reports 8:1835 (2018). DOI:10.1038/s41598-018-20082-0  PDF  Supplementary information
    Error-Gated Hebbian Rule: A Local Learning Rule for Principal and Independent Component Analysis
  22. T. Danjo, T. Toyoizumi, and S. Fujisawa, Science 359, 213-218 (2018). DOI:10.1126/science.aao3898  PDF
    Spatial representations of self and other in the hippocampus
  23. Ł. Kuśmierz and T. Toyoizumi, Physical Review Letters 119, 250601 (2017). DOI:10.1103/PhysRevLett.119.250601  PDF
    Emergence of Lévy walks from second-order stochastic optimization
  24. Ł. Kuśmierz, T. Isomura, and T. Toyoizumi, Current Opinion in Neurobiology 46, 170-177 (2017). DOI:10.1016/j.conb.2017.08.020  PDF
    Learning with three factors: modulating Hebbian plasticity with errors
  25. S. Tajima, T. Mita, D. Bakkum, H. Takahashi, and T. Toyoizumi, Proc. Natl. Acad. Sci. USA 114, 9517-9522 (2017). DOI:10.1073/pnas.1705981114  PDF
    Locally embedded presages of global network bursts
  26. T. Keck, T. Toyoizumi, L. Chen, B. Doiron, D. E. Feldman, K. Fox, W. Gerstner, P. G. Haydon, M. Hubener, H.-K. Lee, J. E. Lisman, T. Rose, F. Sengpiel, D. Stellwagen, M. P. Stryker, G. G. Turrigiano, M. C. van Rossum, Philosophical Transaction of the Royal Society B 372, 1715 (2017). DOI:10.1098/rstb.2016.0158  PDF
    Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions
  27. V. Jacob, A. Mitani, T. Toyoizumi, and K. Fox, Journal of Neurophysiology 117, 4-17 (2017). DOI:10.1152/jn.00289.2016  PDF
    Whisker row deprivation affects the flow of sensory information through rat barrel cortex.
  28. H. Huang and T. Toyoizumi, Physical Review E 94, 062310 (2016). DOI:10.1103/PhysRevE.94.062310  PDF
    Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition
  29. M. Lankarany, J. Heiss, I. Lampl, and T. Toyoizumi, Frontiers in Computational Neuroscience 10:110 (2016). DOI:10.3389/fncom.2016.00110  PDF  Supplementary material
    Simultaneous Bayesian estimation of excitatory and inhibitory synaptic conductances by exploiting multiple recorded trials
  30. T. Isomura and T. Toyoizumi, Scientific Reports 6:28073 (2016). DOI:10.1038/srep28073  PDF  Supplementary information
    A local learning rule for independent component analysis
  31. H. Huang and T. Toyoizumi, Physical Review E 93, 062416 (2016). DOI:10.1103/PhysRevE.93.062416  PDF
    Clustering of neural code words revealed by a first-order phase transition
  32. S. Dasguputa, I. Nishikawa, K. Aihara, and T. Toyoizumi, NIPS Workshop on Modeling and Inference for Dynamics on Complex Interaction Networks (2015). PDF
    Efficient signal processing in random networks that generate variability
  33. S. Tajima, T. Yanagawa, N. Fujii, and T. Toyoizumi, PLOS Computational Biology 11, e1004537 (2015). DOI:10.1371/journal.pcbi.1004537  PDF
    Untangling brain-wide dynamics in consciousness by cross-embedding
  34. H. Huang and T. Toyoizumi, Physical Review E 91, 050101 (2015). DOI:10.1103/PhysRevE.91.050101  PDF
    Advanced mean field theory of the restricted Boltzmann machine
  35. H. Shimazaki, K. Sadeghi, T. Ishikawa, Y. Ikegaya, and T. Toyoizumi, Scientific Reports 5:9821 (2015). DOI:10.1038/srep09821  PDF  Supplementary information
    Simultaneous silence organizes structured higher-order interactions in neural populations.
  36. T. Toyoizumi and H. Huang, Physical Review E 91, 032802 (2015). DOI:10.1103/PhysRevE.91.032802  PDF
    Structure of attractors in randomly connected networks
  37. T. Toyoizumi, M. Kaneko, M. P. Stryker, and K. D. Miller, Neuron 84, 497-510 (2014). DOI:10.1016/j.neuron.2014.09.036  PDF
    Modeling the dynamic interaction of Hebbian and homeostatic plasticity
  38. S. Tajima and T. Toyoizumi, Seitai-no-Kagaku 65, 478-479 (2014). DOI:10.11477/mf.2425200048  PDF
    Understandig large-scale dynamical systems by the embedding theorem (in Japanese)
  39. T. Toyoizumi, H. Miyamoto, Y. Yazaki-Sugiyama, N. Atapour, T. K. Hensch, and K. D. Miller, Neuron 80, 51-63 (2013). DOI:10.1016/j.neuron.2013.07.022  PDF  Supplemental information
    A theory of the transition to critical period plasticity: inhibition selectively suppresses spontaneous activity.
  40. M. Lankarany, W. P. Zhu, M. N. S. Swamy, T. Toyoizumi, Frontiers in Computational Neuroscience 7:109 (2013). DOI:10.3389/fncom.2013.00109  PDF
    Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian Mixture Kalman Filtering
  41. S. Amari, H. Ando, T. Toyoizumi, and N. Masuda, Physical Review E 87, 022814 (2013). DOI:10.1103/PhysRevE.87.022814  PDF
    State concentration exponent as a measure of quickness in Kauffman-type networks
  42. T. Toyoizumi, Neural Computation 24, 2678-2699 (2012). DOI:10.1162/NECO_a_00324  PDF   Color figures
    Nearly extensive sequential memory lifetime achieved by coupled nonlinear neurons
  43. T. Toyoizumi and L. F. Abbott, Physical Review E 84, 051908 (2011). DOI:10.1103/PhysRevE.84.051908  PDF
    Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime
  44. J. Gjorgjieva, T. Toyoizumi and S. J. Eglen, PLoS Computational Biology 5, e1000618 (2009). DOI:10.1371/journal.pcbi.1000618  PDF
    Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus.
  45. T. Toyoizumi and K. D. Miller, Journal of Neuroscience 29, 6514-6525 (2009). DOI:10.1523/JNEUROSCI.0492-08.2009  PDF  Supplemental materials
    Equalization of ocular dominance columns induced by an activity-dependent learning rule and the maturation of inhibition
  46. T. Toyoizumi, K. Rahnama Rad and L. Paninski, Neural Computation 21, 1203-1243 (2009). DOI:10.1162/neco.2008.04-08-757  PDF  Color figures
    Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness
  47. Y. Sato, T. Toyoizumi and K. Aihara, Neural Computation 19, 3335-3355 (2007). DOI:10.1162/neco.2007.19.12.3335  PDF
    Bayesian inference explains perception of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli.
  48. D. Sussillo, T. Toyoizumi and W. Maass, Journal of Neurophysiology 97, 4079-4095 (2007). DOI:10.1152/jn.01357.2006  PDF  Supplementary material
    Self-tuning of neural circuits through short-term synaptic plasticity
  49. T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Neural Computation 19, 639-671 (2007). DOI:10.1162/neco.2007.19.3.639  PDF
    Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution
  50. T. Toyoizumi, K. Aihara and S. Amari, Physical Review Letters 97, 098102 (2006). DOI:10.1103/PhysRevLett.97.098102  PDF
    Fisher Information for Spike-Based Population Decoding
  51. T. Toyoizumi and K. Aihara, Journal of the Society of Instrument and Control Engineers 45, 741-747 (2006). DOI:10.11499/sicejl1962.45.74  PDF
    A Synaptic Plasticity Rule Derived Based on the Information Maximization Principle and Firing Rate Control (A review in Japanese)
  52. J.-P. Pfister, T. Toyoizumi, D. Barber and W. Gerstner, Neural Computation 18, 1318-1348 (2006). DOI:10.1162/neco.2006.18.6.1318  PDF
    Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing
  53. T. Toyoizumi and K. Aihara, International Journal of Bifurcation and Chaos 16, 129-136 (2006). DOI:10.1142/S0218127406014630  PDF
    Generalization of the mean-field method for power-law distributions
  54. T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Proc. Natl. Acad. Sci. USA 102, 5239-5244 (2005). DOI:10.1073/pnas.0500495102  PDF  Supporting text
    Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission
  55. T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Advances in Neural Information Processing Systems 17, 1409-1416 (2005). PDF
    Spike-timing dependent Plasticity and mutual information maximization for a spiking neuron model
  56. T. Toyoizumi and K. Aihara, Transactions of the Institute of Electronics 86-D2, 959-965 (2003). PDF
    Mean-field and Variational Methods for alpha-families (in Japanese)
  57. T. Sasamoto, T. Toyoizumi and H. Nishimori, Journal of Physics A 34, 9555-9567 (2001). DOI:10.1088/0305-4470/34/44/314  PDF
    Statistical mechanics of an NP-complete problem: Subset sum