Faculty Detail / 研究室詳細

Hiroyuki Nakahara, Ph.D.

- What are neural computations and mathematical foundations for decision & motivated/social behaviors?

Integrated Theoretical Neuroscience

Senior Team Leader

Computational neuroscience, Decision making, Motivated/social behavior

Hiroyuki  Nakahara

Research Area

We build mathematical and computational models of neural processes to understand brain functions. We are particularly interested in the neural mechanisms used for decision making and reward-related behavior, including social intelligence and motivation. In relation to this main subject, we investigate the coding capability of neural popurationsand neural circuits at the interface between systems and cellular neuroscience. We also emphasize close collaboration with experimental sutdies.

Selected Publications View All

  1. 1

    Sugiyama M, Nakahara H, and Tsuda K: "Information Decomposition on Structured Space.", IEEE International Symposium on Information Theory 2016, 575-579 (2016)

  2. 2

    Nakahara H: "Multiplexing signals in reinforcement learning with internal models and dopamine.", Curr Opin Neurobiol, 25C, 123-129 (2014)

  3. 3

    Nakahara H, and Hikosaka O: "Learning to represent reward structure: A key to adapting to complex environments.", Neurosci Res (2012)

  4. 4

    Suzuki S, Harasawa N, Ueno K, Gardner JL, Ichinohe N, Haruno M, Cheng K, and Nakahara H: "Learning to simulate others' decisions.", Neuron, 74(6), 1125-37 (2012)

  5. 5

    Nakahara H, and Kaveri S: "Internal-time temporal difference model for neural value-based decision making.", Neural Comput, 22(12), 3062-106 (2010)

  6. 6

    Bromberg-Martin ES, Matsumoto M, Nakahara H, and Hikosaka O: "Multiple timescales of memory in lateral habenula and dopamine neurons.", Neuron, 67(3), 499-510 (2010)

  7. 7

    Santos GS, Gireesh ED, Plenz D, and Nakahara H: "Hierarchical interaction structure of neural activities in cortical slice cultures.", J Neurosci, 30(26), 8720-33 (2010)

  8. 8

    Nakahara H, Amari S, and Richmond BJ: "A comparison of descriptive models of a single spike train by information-geometric measure.", Neural Comput, 18(3), 545-68 (2006)

  9. 9

    Nakahara H, Itoh H, Kawagoe R, Takikawa Y, and Hikosaka O: "Dopamine neurons can represent context-dependent prediction error.", Neuron, 41(2), 269-80 (2004)

  10. 10

    Nakahara H, and Amari S: "Information-geometric measure for neural spikes.", Neural Comput, 14(10), 2269-316 (2002)

  11. 11

    Nakahara H, Doya K, and Hikosaka O.: "Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuomotor sequences - a computational approach.", J Cogn Neurosci, 13(5), 626-47 (2001)

Press Releases View All