Faculty Detail / 研究室詳細

Taro Toyoizumi, Ph.D.

- We investigate the computational principles that govern experience-based organization of neural circuits.

Neural Computation and Adaptation

Team Leader

Modeling of activity-dependent plasticity in the brain, Theoretical neuroscience

Taro  Toyoizumi

Research Area

Our research is in the field of Computational Neuroscience. Computer models are used to study how information is processed in the brain and how the brain circuits adapt to and learn from the environment. We employ analytical techniques from statistical physics and information theory to investigate key functional properties for neuronal circuits. We use these techniques to reduce diverse experimental findings into a few core concepts that robustly explain the phenomena of interest. We are particularly interested in activity-dependent forms of plasticity in the brain, which are known to have large impacts on learning, memory, and development. With the aid of mathematical models, we seek a theory that unites the cellular level plasticity rules and the circuit level adaptation in different brain areas and animal species. Efficacy of neurons to represent and retain information is estimated from the structure and behavior of resulting circuits.

Selected Publications View All

  1. 1

    Lankarany M, Heiss J, and Lampl I and Toyoizumi T: "Simultaneous Bayesian estimation of excitatory and inhibitory synaptic conductances by exploiting multiple recorded trials", Frontiers in Computational Neuroscience( 10:110 ) (2016)

  2. 2

    Isomura T, and Toyoizumi T: "A Local Learning Rule for Independent Component Analysis.", Sci Rep, 6, 28073 (2016)

  3. 3

    Huang H and Toyoizumi T: "Clustering of neural code words revealed by a first-order phase transition", Physical Review E, 93, 062416 (2016)

  4. 4

    Tajima S, Yanagawa T, Fujii N, and Toyoizumi T: "Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.", PLoS Comput Biol, 11(11), e1004537 (2015)

  5. 5

    Huang H, and Toyoizumi T: "Advanced mean-field theory of the restricted Boltzmann machine.", Phys Rev E Stat Nonlin Soft Matter Phys, 91(5-1), 050101 (2015)

  6. 6

    Shimazaki H, Sadeghi K, Ishikawa T, Ikegaya Y, and Toyoizumi T: "Simultaneous silence organizes structured higher-order interactions in neural populations.", Sci Rep, 5, 9821 (2015)

  7. 7

    Toyoizumi T, Kaneko M, Stryker MP, and Miller KD: "Modeling the dynamic interaction of Hebbian and homeostatic plasticity", Neuron, 84(2), 497-510 (2014)

  8. 8

    Toyoizumi T, Miyamoto H, Yazaki-Sugiyama Y, Atapour N, Hensch TK, and Miller KD: "A theory of the transition to critical period plasticity: inhibition selectively suppresses spontaneous activity", Neuron, 80(1), 51-63 (2013)

  9. 9

    Toyoizumi T and Abbott LF : "Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime", Physical Review, E 84(5), 051908 (2011)

  10. 10

    Toyoizumi T, Aihara K, and Amari S: "Fisher information for spike-based population decoding.", Phys Rev Lett, 97(9), 98102 (2006)

  11. 11

    Toyoizumi T, Pfister JP, Aihara K, and Gerstner W: "Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission.", Proc Natl Acad Sci U S A, 102(14), 5239-44 (2005)

Press Releases View All