Publications

ORCiD, WoS, GoogleScholar


  1. M.A.K. Peters, S.B. Michael Yoo, M. Klincewicz, T. Toyoizumi, B. Richards, T. Webb, H. Lau, Current Biology 36, R821-R832 (2026). https://doi.org/10.1016/j.cub.2026.06.020
    Looking to the brain to improve energy efficiency of AI

  2. S. Sato, T. Kato, T. Toyoizumi, Schizophrenia Bulletin, accepted. https://www.biorxiv.org/content/10.64898/2026.01.28.702194v1
    A dual prediction error populations model of schizophrenia spectrum disorders: bridging NMDA hypofunction and gain hyperfunction

  3. Y. Kinoshita, N. Nishikawa, and T. Toyoizumi, ICML2026, accepted. https://doi.org/10.48550/arXiv.2603.14830
    Dataset distillation efficiently encodes low-dimensional representations from gradient-based learning of non-linear tasks

  4. Y. Ito and T. Toyoizumi, eLife 14, RP106506 (2026). https://doi.org/10.7554/eLife.106506.4
    Modeling flexible behavior with remapping-based hippocampal sequence learning

  5. G. Shimizu, T. Toyoizumi, International Conference on Neural Information Processing 414-428 (2025). https://doi.org/10.1007/978-981-95-4378-6_29
    Diverse neural sequences in QIF networks: An analytically tractable framework for synfire chains and hippocampal replay

  6. A. Ouchi, T. Toyoizumi, N. Matsumoto, Y. Ikegaya, eLife 14, e97270 (2025). https://doi.org/10.7554/eLife.97270
    Distributed subthreshold representation of sharp wave-ripples by hilar mossy cells

  7. K. Yoshida and T. Toyoizumi, Science Advances 11, eadp9048 (2025). https://doi.org/10.1126/sciadv.adp9048
    A biological model of nonlinear dimensionality reduction

  8. Y. Kinoshita and T. Toyoizumi, NeurIPS (2024). https://doi.org/10.48550/arXiv.2404.09821  
    A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness

  9. T. Sawada, Y. Iino, K. Yoshida, H. Okazaki, S. Nomura, C. Shimizu, T. Arima, M. Juichi, S. Zhou, N. Kurabayashi, T. Sakurai, S. Yagishita, M. Yanagisawa, T. Toyoizumi, H. Kasai, and S. Shi, Science 385:1459-1465 (2024). https://doi.org/10.1126/science.adl3043  
    Prefrontal synaptic regulation of homeostatic sleep pressure revealed through synaptic chemogenetics.

  10. Y. Terada and T. Toyoizumi, Proc. Natl. Acad. Sci. USA 121:18, e2312992121 (2024). https://doi.org/10.1073/pnas.2312992121   PDF
    Chaotic neural dynamics facilitate probabilistic computations through sampling

  11. L. Kang and T. Toyoizumi, Nature Communications 15, 647 (2024). https://doi.org/10.1038/s41467-024-44877-0   PDF
    Distinguishing examples while building concepts in hippocampal and artificial networks.

  12. H. K. Chan and T. Toyoizumi, Scientific Reports 14, 657 (2024). https://doi.org/10.1038/s41598-023-50529-y   PDF
    A multi-stage anticipated surprise model with dynamic expectation for economic decision-making

  13. K. Yoshida and T. Toyoizumi, Current Opinion in Neurobiology 83, 102799 (2023). https://doi.org/10.1016/j.conb.2023.102799   PDF
    Computational role of sleep in memory reorganization

  14. A. Nejatbakhsh, F. Fumarola, S. Esteki, T. Toyoizumi, R. Kiani, and L. Mazzucato, Physical Review Research 5, 043211 (2023). https://doi.org/10.1103/PhysRevResearch.5.043211   PDF
    Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics

  15. L. Kang and T. Toyoizumi, Physical Review E 108, 054410 (2023). https://doi.org/10.1103/PhysRevE.108.054410   arXiv
    Hopfield-like network with complementary encodings of memories

  16. K. Yoshida and T. Toyoizumi, PNAS Nexus 2, 1-13 (2023). https://doi.org/10.1093/pnasnexus/pgac286   PDF
    Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep.

  17. Z. He and T. Toyoizumi, Neural Computation 35, 38-57 (2023). https://doi.org/10.1162/neco_a_01542   PDF
    Progressive interpretation synthesis: interpreting task solving by quantifying previously used and unused information

  18. T. Toyoizumi, Proc. Natl. Acad. Sci. USA 119:48, e2216092119 (2022). https://doi.org/10.1073/pnas.2216092119   PDF
    Ordering in heterogeneous connectome weights for visual information processing

  19. A. Marzoll, K. Shibata, T. Toyoizumi, I. Chavva, T. Watanabe, iScience 25, 105492 (2022). https://doi.org/10.1016/j.isci.2022.105492   PDF
    Decrease in signal-related activity by visual training and repetitive visual stimulation

  20. F. Fumarola, Z. He, Ł. Kuśmierz, and T. Toyoizumi, Physical Review Research 4, 033089 (2022). https://doi.org/10.1103/PhysRevResearch.4.033089   PDF
    Decoding silence in free recall

  21. G. Shimizu, K. Yoshida, H. Kasai, and T. Toyoizumi, Current Opinion in Neurobiology 70, 34-42 (2021). https://doi.org/10.1016/j.conb.2021.06.002   PDF
    Computational roles of intrinsic synaptic dynamics

  22. H. Kasai, N. E. Ziv, H. Okazaki, S. Yagishita, and T. Toyoizumi, Nature Reviews Neuroscience 22, 407-422 (2021). https://doi.org/10.1038/s41583-021-00467-3   PDF
    Spine dynamics in the brain, mental disorders and artificial neural networks

  23. T. Isomura and T. Toyoizumi, Nature Machine Intelligence 3, 434-446 (2021). https://doi.org/10.1038/s42256-021-00306-1   PDF
    Dimensionality reduction to maximize prediction generalization capability

  24. Ł. Kuśmierz and T. Toyoizumi, Proc. Natl. Acad. Sci. USA 118:10, e2024297118 (2021). https://doi.org/10.1073/pnas.2024297118   PDF
    Infection curves on small-world networks are linear only in the vicinity of the critical point

  25. Y. Ito and T. Toyoizumi, PLOS Computational Biology 17, e1008700 (2021). https://doi.org/10.1371/journal.pcbi.1008700   PDF
    Learning poly-synaptic paths with traveling waves

  26. T. Isomura and T. Toyoizumi, Neural Computation 33, 1433-1468 (2021). https://doi.org/10.1162/neco_a_01378   PDF
    On the achievability of blind source separation for high-dimensional nonlinear source mixtures

  27. Ł. Kuśmierz, S. Ogawa, and T. Toyoizumi, Physical Review Letters 125, 028101 (2020). https://doi.org/10.1103/PhysRevLett.125.028101   PDF   Supplemental material
    Edge of chaos and avalanches in neural networks with heavy-tailed synaptic weight distribution

  28. R. Legaspi and T. Toyoizumi, Nature Communications 10:4250 (2019). https://doi.org/10.1038/s41467-019-12170-0   PDF
    A Bayesian psychophysics model of sense of agency

  29. Ł. Kuśmierz and T. Toyoizumi, Physical Review E 100, 032110 (2019). https://doi.org/10.1103/PhysRevE.100.032110   PDF
    Robust random search with scale-free stochastic resetting

  30. J. Humble, K. Hiratsuka, H. Kasai, and T. Toyoizumi, Frontiers in Computational Neuroscience 13:38 (2019) https://doi.org/10.3389/fncom.2019.00038   PDF
    Intrinsic Spine Dynamics Are Critical for Recurrent Network Learning in Models With and Without Autism Spectrum Disorder.

  31. T. Isomura and T. Toyoizumi, Scientific Reports 9:7127 (2019). https://doi.org/10.1038/s41598-019-43423-z  PDF
    Multi-context blind source separation by error-gated Hebbian rule

  32. R. Legaspi, Z. He and T. Toyoizumi, Current Opinion in Behavioral Sciences 29:84-90 (2019). https://doi.org/10.1016/j.cobeha.2019.04.004   PDF
    Synthetic Agency: Sense of Agency in Artificial Intelligence

  33. E. Munro Krull, S. Sakata and T. Toyoizumi, Frontiers in Neuroscience 13:316 (2019). https://doi.org/10.3389/fnins.2019.00316  PDF
    Theta oscillations alternate with high amplitude neocortical population within synchronized states.

  34. 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). https://doi.org/10.1016/j.neulet.2018.02.006  PDF
    Calcineurin knockout mice show a selective loss of small spines.

  35. C. L. Buckley and T. Toyoizumi, PLOS Computational Biology 14, e1005926 (2018). https://doi.org/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

  36. T. Isomura and T. Toyoizumi, Scientific Reports 8:1835 (2018). https://doi.org/10.1038/s41598-018-20082-0  PDF  Supplementary information
    Error-Gated Hebbian Rule: A Local Learning Rule for Principal and Independent Component Analysis

  37. T. Danjo, T. Toyoizumi, and S. Fujisawa, Science 359, 213-218 (2018). https://doi.org/10.1126/science.aao3898  PDF
    Spatial representations of self and other in the hippocampus

  38. Ł. Kuśmierz and T. Toyoizumi, Physical Review Letters 119, 250601 (2017). https://doi.org/10.1103/PhysRevLett.119.250601  PDF
    Emergence of Lévy walks from second-order stochastic optimization

  39. Ł. Kuśmierz, T. Isomura, and T. Toyoizumi, Current Opinion in Neurobiology 46, 170-177 (2017). https://doi.org/10.1016/j.conb.2017.08.020  PDF
    Learning with three factors: modulating Hebbian plasticity with errors

  40. S. Tajima, T. Mita, D. Bakkum, H. Takahashi, and T. Toyoizumi, Proc. Natl. Acad. Sci. USA 114, 9517-9522 (2017). https://doi.org/10.1073/pnas.1705981114  PDF
    Locally embedded presages of global network bursts

  41. 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). https://doi.org/10.1098/rstb.2016.0158  PDF
    Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions

  42. V. Jacob, A. Mitani, T. Toyoizumi, and K. Fox, Journal of Neurophysiology 117, 4-17 (2017). https://doi.org/10.1152/jn.00289.2016  PDF
    Whisker row deprivation affects the flow of sensory information through rat barrel cortex.

  43. H. Huang and T. Toyoizumi, Physical Review E 94, 062310 (2016). https://doi.org/10.1103/PhysRevE.94.062310  PDF
    Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition

  44. M. Lankarany, J. Heiss, I. Lampl, and T. Toyoizumi, Frontiers in Computational Neuroscience 10:110 (2016). https://doi.org/10.3389/fncom.2016.00110  PDF  Supplementary material
    Simultaneous Bayesian estimation of excitatory and inhibitory synaptic conductances by exploiting multiple recorded trials

  45. T. Isomura and T. Toyoizumi, Scientific Reports 6:28073 (2016). https://doi.org/10.1038/srep28073  PDF  Supplementary information
    A local learning rule for independent component analysis

  46. H. Huang and T. Toyoizumi, Physical Review E 93, 062416 (2016). https://doi.org/10.1103/PhysRevE.93.062416  PDF
    Clustering of neural code words revealed by a first-order phase transition

  47. 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

  48. S. Tajima, T. Yanagawa, N. Fujii, and T. Toyoizumi, PLOS Computational Biology 11, e1004537 (2015). https://doi.org/10.1371/journal.pcbi.1004537  PDF
    Untangling brain-wide dynamics in consciousness by cross-embedding

  49. H. Huang and T. Toyoizumi, Physical Review E 91, 050101 (2015). https://doi.org/10.1103/PhysRevE.91.050101  PDF
    Advanced mean field theory of the restricted Boltzmann machine

  50. H. Shimazaki, K. Sadeghi, T. Ishikawa, Y. Ikegaya, and T. Toyoizumi, Scientific Reports 5:9821 (2015). https://doi.org/10.1038/srep09821  PDF  Supplementary information
    Simultaneous silence organizes structured higher-order interactions in neural populations.

  51. T. Toyoizumi and H. Huang, Physical Review E 91, 032802 (2015). https://doi.org/10.1103/PhysRevE.91.032802  PDF
    Structure of attractors in randomly connected networks

  52. T. Toyoizumi, M. Kaneko, M. P. Stryker, and K. D. Miller, Neuron 84, 497-510 (2014). https://doi.org/10.1016/j.neuron.2014.09.036  PDF
    Modeling the dynamic interaction of Hebbian and homeostatic plasticity

  53. S. Tajima and T. Toyoizumi, Seitai-no-Kagaku 65, 478-479 (2014). https://doi.org/10.11477/mf.2425200048  PDF
    Understandig large-scale dynamical systems by the embedding theorem (in Japanese)

  54. T. Toyoizumi, H. Miyamoto, Y. Yazaki-Sugiyama, N. Atapour, T. K. Hensch, and K. D. Miller, Neuron 80, 51-63 (2013). https://doi.org/10.1016/j.neuron.2013.07.022  PDF  Supplemental information
    A theory of the transition to critical period plasticity: inhibition selectively suppresses spontaneous activity.

  55. M. Lankarany, W. P. Zhu, M. N. S. Swamy, T. Toyoizumi, Frontiers in Computational Neuroscience 7:109 (2013). https://doi.org/10.3389/fncom.2013.00109  PDF
    Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian Mixture Kalman Filtering

  56. S. Amari, H. Ando, T. Toyoizumi, and N. Masuda, Physical Review E 87, 022814 (2013). https://doi.org/10.1103/PhysRevE.87.022814  PDF
    State concentration exponent as a measure of quickness in Kauffman-type networks

  57. T. Toyoizumi, Neural Computation 24, 2678-2699 (2012). https://doi.org/10.1162/NECO_a_00324  PDF   Color figures
    Nearly extensive sequential memory lifetime achieved by coupled nonlinear neurons

  58. T. Toyoizumi and L. F. Abbott, Physical Review E 84, 051908 (2011). https://doi.org/10.1103/PhysRevE.84.051908  PDF
    Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime

  59. J. Gjorgjieva, T. Toyoizumi and S. J. Eglen, PLoS Computational Biology 5, e1000618 (2009). https://doi.org/10.1371/journal.pcbi.1000618  PDF
    Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus.

  60. T. Toyoizumi and K. D. Miller, Journal of Neuroscience 29, 6514-6525 (2009). https://doi.org/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

  61. T. Toyoizumi, K. Rahnama Rad and L. Paninski, Neural Computation 21, 1203-1243 (2009). https://doi.org/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

  62. Y. Sato, T. Toyoizumi and K. Aihara, Neural Computation 19, 3335-3355 (2007). https://doi.org/10.1162/neco.2007.19.12.3335  PDF
    Bayesian inference explains perception of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli.

  63. D. Sussillo, T. Toyoizumi and W. Maass, Journal of Neurophysiology 97, 4079-4095 (2007). https://doi.org/10.1152/jn.01357.2006  PDF  Supplementary material
    Self-tuning of neural circuits through short-term synaptic plasticity

  64. T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Neural Computation 19, 639-671 (2007). https://doi.org/10.1162/neco.2007.19.3.639  PDF
    Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution

  65. T. Toyoizumi, K. Aihara and S. Amari, Physical Review Letters 97, 098102 (2006). https://doi.org/10.1103/PhysRevLett.97.098102  PDF
    Fisher Information for Spike-Based Population Decoding

  66. T. Toyoizumi and K. Aihara, Journal of the Society of Instrument and Control Engineers 45, 741-747 (2006). https://doi.org/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)

  67. J.-P. Pfister, T. Toyoizumi, D. Barber and W. Gerstner, Neural Computation 18, 1318-1348 (2006). https://doi.org/10.1162/neco.2006.18.6.1318  PDF
    Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing

  68. T. Toyoizumi and K. Aihara, International Journal of Bifurcation and Chaos 16, 129-136 (2006). https://doi.org/10.1142/S0218127406014630  PDF
    Generalization of the mean-field method for power-law distributions

  69. T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Proc. Natl. Acad. Sci. USA 102, 5239-5244 (2005). https://doi.org/10.1073/pnas.0500495102  PDF  Supporting text
    Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission

  70. 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

  71. 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)

  72. T. Sasamoto, T. Toyoizumi and H. Nishimori, Journal of Physics A 34, 9555-9567 (2001). https://doi.org/10.1088/0305-4470/34/44/314  PDF
    Statistical mechanics of an NP-complete problem: Subset sum