Laboratory for Neural Computation and Adaptation
Laboratory Head
Taro TOYOIZUMI (Ph.D.)
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.
- Influences of synaptic connectivity and the resulting dynamical state on information representation and the dynamical memory capacity in the brain
- Modeling of the interaction of Hebbian and homeostatic plasticity
- Unifying description of activity-dependent plasticity across different developmental stages and animal species
- Optimal Synaptic plasticity rules for information transmission
- T. Toyoizumi and L. F. Abbott,:
"Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime"
Physical Review E 84, 051908 (2011) - J. Gjorgjieva, T. Toyoizumi and S. J. Eglen,:
"Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus"
PLoS Computational Biology 5, e1000618 (2009) - T. Toyoizumi and K. D. Miller:
"Equalization of ocular dominance columns induced by an activity-dependent learning rule and the maturation of inhibition"
J. Neuroscience 29, 6514-6525 (2009) - T. Toyoizumi, K. Rahnama Rad and L. Paninski,:
"Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness"
Neural Computation 21, 1203-1243 (2009) - Y. Sato, T. Toyoizumi and K. Aihara,:
"Bayesian inference explains perception of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli."
Neural Computation 19, 3335-3355 (2007) - T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner,:
"Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution"
Neural Computation 19, 639-671 (2007) - T. Toyoizumi, K. Aihara and S. Amari,:
"Fisher Information for Spike-Based Population Decoding"
Phys. Rev. Lett. 97, 098102 (2006) - T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner,:
"Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission"
Proc. Natl. Acad. Sci. USA 102, 5239-5244 (2005) - T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner,:
"Spike-timing dependent Plasticity and mutual information maximization for a spiking neuron model"
Advances in Neural Information processing Systems 17, 1409-1416 (2005)
Principal Investigator
- Taro TOYOIZUMI
- Laboratory Head
Members
- Hideaki SHIMAZAKI
- Research Scientist
- Christopher Laurie BUCKLEY
- Research Scientist
- Erin Christine MUNRO
- Research Scientist
- Reiko KIYOTAKI
- Assistant
- Isao NISHIKAWA
- Visiting Scientist