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Laboratory for Neural Coding and Brain Computing

Welcome to Fukai Lab!

Overview

  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.

Current research interests

  • Stochastic computation by the brain and its circuit mechanism
  • Neural dynamics and information representations
  • Theoretical/experimental studies of cortical microcircuit functions
  • Information analysis of neural ensemble data

Major publications


  • Teramae J, Tsubo Y, Fukai T (2012) Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links, Scientific Reports 2, 485. doi:10.1038/srep00485.
  • Igarashi J, Isomura Y, Arai K, Harukuni R and Fukai T (2012) A theta-gamma oscillation code for neuronal coordination during motor behavior, J Neurosci. 33: 18515-18530
  • Tsubo Y, Isomura Y, Fukai T (2012) Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons, PLoS Computational Biology, e1002461.
  • Gilson M, Fukai T (2011) Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma, PLoS ONE 6(10), e25339.
  • Isomura Y, Harukuni R, Takekawa T, Aizawa H, Fukai T (2009) Microcircuitry coordination of cortical motor information in self-initiation of voluntary movements, Nature Neuroscience 12, 1586-1593.
  • Okamoto H, Fukai T. (2009) Recurrent network models for perfect temporal integration of fluctuating correlated inputs, PLoS Comput. Biol. 5(6) e1000404.
  • Sakai Y, Fukai T (2008) When does reward maximization lead to matching law? PLoS ONE 3(11), e3795.
  • Fukai T, Kanemura S (2001) Noise-tolerant stimulus discrimination by synchronization with depressing synapses, Biological Cybernetics 85, 107-116.
  • Okamoto H, Fukai T (2001) A neural mechanism for cognitive timer, Physical Review Letters 86, 3919-3922.
  • Fukai T (2000) Neuronal communication within synchronous gamma oscillations, NeuroReport 11, 3457-3460.
  • Fukai T (1999) Sequence generation in arbitrary temporal patterns from theta-nested gamma oscillations: A model of the basal ganglia-thalamo-cortical loops. Neural Networks 12, 975-987.
  • Fukai T, Tanaka S (1997) A simple neural network exhibiting selective activation of neuronal ensembles: From winner-take-all to winners-share-all, Neural Computation 9, 77-97.
  • Shiino M, Fukai T (1993) Self-consistent signal-to-noise analysis of the statistical behavior of analog neural networks & enhancement of the storage capacity, Physical Review E48, 867-897.

eLIFE_cover

News & Topics

2018/10/03 Publications
Asabuki T, Hiratani N, and Fukai T. Interactive reservoir computing for chunking information streams. PLoS Comput Biol (in press)

Haga T and Fukai T. Dendritic processing of spontaneous neuronal sequences for single-trial learning. Scientific Reports (in press)


2018/07/10 Publications
Tatsuya Haga and Tomoki Fukai. Recurrent network model for learning goal-directed sequences through reverse replay. eLIFE

Martín-Vázquez G, Asabuki T, Isomura Y and Fukai T. Learning Task-Related Activities From Independent Local-Field-Potential Components Across Motor Cortex Layers. Front. Neurosci

Chi Chung Alan Fung and Tomoki Fukai.Transient and Persistent UP States during Slow-wave Oscillation and their Implications for Cell-Assembly Variability.Sci Rep

Naoki Hiratani and Tomoki Fukai. Redundancy in synaptic connections enables neurons to learn optimally. PNAS


2017/10/17 Publications
Naoki Hiratani and Tomoki Fukai. Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity. The Journal of Neuroscience


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Lab. for Neural Coding and Brain Computing, RIKEN CBS
2-1 Hirosawa, Wako, Saitama, 351-0198 JAPAN