What are the principles of neural information processing in the brain? This is the question we want to address.
Tomoki Fukai, Ph.D.
Team Leader, Neural Coding and Brain Computing
tfukai [at] riken.jp
High-level functions of the brain, such as perception, learning and memory, decision making, etc., emerge from computations by neuronal networks. My lab uses theoretical and electrophysiological approaches to better understand the fundamental properties of neural networks.
Uncovering the circuit mechanism is particularly important as I consider that most of the advantages of brain's computation reside in the way the brain implements it by neural circuits. The brain is believed to utilize noise for modeling the external world for performing robust and flexible computations in sensory perception, decision making, and so on. The low energy consumption of the brain (~a few tens of watts) also suggests that the powerful computations performed by the brain do not require a code with a clear separation between signals and noise. The goal of our research is to uncover the principles of the brain's stochastic computation and to provide the theoretical basis for creating brain-style computing machines.
- Handa, T., Takekawa, T., Harukuni, R., Isomura, Y., and Fukai, T.:
"Medial Frontal Circuit Dynamics Represents Probabilistic Choices for Unfamiliar Sensory Experience."
Cerebral Cortex 27, 3818-3831 (2017).
- Omura, Y., Carvalho, M.M., Inokuchi, K., and Fukai, T.:
"A lognormal recurrent network model for burst generation during hippocampal sharp waves."
The Journal of Neuroscience 35, 14585-14601 (2015).
- Hiratani, N., and Fukai, T.:
"Mixed signal learning by spike correlation propagation in feedback inhibitory circuits."
PLoS Computational Biology 11, e1004227, 1-36 (2015).
- Igarashi, J., Isomura, Y., Arai, K., Harukuni, R., and Fukai, T.:
"A θ-γ oscillation code for neuronal coordination during motor behavior."
The Journal of Neuroscience 33, 18515–18530 (2013).
- Teramae, J., Tsubo, Y., and Fukai, T.:
"Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links."
Scientific Reports 2, 485, 1-6 (2012).
- Tsubo, Y., Isomura, Y., and Fukai, T.:
"Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons."
PLoS Computational Biology 8, e1002461, 1-11 (2012).
- Yazaki-Sugiyama, Y., Kang, S., Cateau, H., Fukai, T., and Hensch, T.K.:
"Bidirectional plasticity in fast-spiking GABA circuits by visual experience."
Nature 462, 218-221 (2009).
- Isomura, Y., Harukuni, R., Takekawa, T., Aizawa, H., and Fukai, T.:
"Microcircuitry coordination of cortical motor information in self-initiation of voluntary movements."
Nature Neuroscience 12, 1586-1593 (2009).
- Teramae, J., and Fukai, T.:
"Temporal precision of spike response to fluctuating input in pulse-coupled networks of oscillating neurons."
Physical Review Letters 101, 248105, 1-4 (2008).
- Miura, K., Tsubo, Y., Okada, M., and Fukai, T.:
"Balanced excitatory and inhibitory inputs to cortical neurons decouple firing irregularity from rate modulations."
The Journal of Neuroscience 27, 13802-13812 (2007).