Neural Circuit Theory
Senior Team Leader
Computational neuroscience, Microcircuits, Multiunit recordings
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.
How the brain processes information crucially depends on how cortical local circuits are organized. Spontaneous neuronal activity propagating through neocortical slices displays highly diverse, yet repeatable activity patterns called “neuronal avalanches". A crucial properties of neuronal avalanches are the power-law distributions of the event sizes and lifetimes. Assuming that these properties reflect the topological structure of local cortical circuits, we succeeded to model a neuronal network that can propagate avalanche-like spiking activity. In particular, we derived a neuronal wiring rule that governs the formation of this network (lower figures). The mathematical structure of the wiring rule quite resembles that of a simple dynamical model of avalanches (upper figures). Our model predicts that local cortical circuits comprise mutually overlapping cell assemblies that can be viewed as a complex mixture of feedforward chains and recurrent circuits. Local cortical circuits may have a more complex topological design than has previously been thought.
Omura Y, Carvalho MM, Inokuchi K, and Fukai T: "A Lognormal Recurrent Network Model for Burst Generation during Hippocampal Sharp Waves.", J Neurosci, 35(43), 14585-601 (2015)
Hiratani N, and Fukai T: "Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits.", PLoS Comput Biol, 11(4), e1004227 (2015)
Igarashi J, Isomura Y, Arai K, Harukuni R, and Fukai T: "A θ-γ Oscillation Code for Neuronal Coordination during Motor Behavior.", J Neurosci, 33(47), 18515-18530 (2013)
Tsubo Y, Isomura Y, and Fukai T: "Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons.", PLoS Comput Biol, 8(4), e1002461 (2012)
Teramae JN, Tsubo Y, and Fukai T: "Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links.", Sci Rep, 2, 485 (2012)
Takekawa T, Isomura Y, and Fukai T: "Accurate spike sorting for multi-unit recordings.", Eur J Neurosci, 31(2), 263-72 (2010)
Isomura Y, Harukuni R, Takekawa T, Aizawa H, and Fukai T: "Microcircuitry coordination of cortical motor information in self-initiation of voluntary movements.", Nat Neurosci, 12(12), 1586-93 (2009)
Yazaki-Sugiyama Y, Kang S, Câteau H, Fukai T, and Hensch TK: "Bidirectional plasticity in fast-spiking GABA circuits by visual experience.", Nature, 462(7270), 218-21 (2009)
Okamoto H, and Fukai T: "Recurrent network models for perfect temporal integration of fluctuating correlated inputs.", PLoS Comput Biol, 5(6), e1000404 (2009)
Teramae JN, and Fukai T: "Temporal precision of spike response to fluctuating input in pulse-coupled networks of oscillating neurons.", Phys Rev Lett, 101(24), 248105 (2008)
Miura K, Tsubo Y, Okada M, and Fukai T: "Balanced excitatory and inhibitory inputs to cortical neurons decouple firing irregularity from rate modulations.", J Neurosci, 27(50), 13802-12 (2007)