Profile

Taro Toyoizumi（豊泉 太郎）

Adjunct Professor,
Department of Mathematical Informatics, Graduate School of Information
Science and Technology, University of Tokyo

RIKEN Center for Brain Science, Team Leader

2-1 Hirosawa, Wako-shi, Saitama 351-0198
Tel: +81-48-467-9644
Fax: +81-48-467-9670

E-mail：taro.toyoizumi@riken.jp

[Home Page]

Curriculum Vitae

Research Themes

Computational Neuroscience, Theory of Neural Adaptation using
Statistical Physics and Information Theory Tools

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

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

Buckley C L and Toyoizumi T.: “A theory of how active behavior
stabilizes neural activity: neural gain modulation by closed-loop
environmental feedback”
PLOS Computational Biology , 14, e1005926 (2018), doi:
10.1371/journal.pcbi.1005926

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

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

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

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), doi: 10.1038/srep09821

Toyoizumi T, Kaneko M, Stryker MP, and Miller KD.: “Modeling the
dynamic interaction of Hebbian and homeostatic plasticity”
Neuron, 84(2), 497-510 (2014), doi: 10.1016/j.neuron.2014.09.036

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), doi: 10.1016/j.neuron.2013.07.022

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), doi: 10.1103/PhysRevE.84.051908

Toyoizumi T, Aihara K, and Amari S.: “Fisher information for
spike-based population decoding.”
Phys Rev Lett, 97(9), 98102 (2006), doi: 10.1103/PhysRevLett.97.098102

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), doi: 10.1073/pnas.0500495102