Hiroyuki Nakahara, Ph.D.

Research Overview

The long-term goal of our laboratory is to understand the computational principles that underlie the way neural systems realize adaptive behavior, decision-making, and associated learning. In particular, we focus on (1) reward-based learning and decision-making and (2) social learning and decision-making. Toward this goal, we address computational questions by building computational and mathematical models. We also use human fMRI in combination with quantitative approaches such as model-based analysis and neural decoding techniques. We further develop quantitative techniques and methods for realizing innovative data analysis in neuroscience, and also theories and models for brain-based artificial intelligence. In collaborative work with experimental investigators, we investigate the topics of our interest, using various types of experimental data.

Main Research Fields

Complex Systems

Related Research Fields

Informatics / Social Sciences / Interdisciplinary Science & Engineering / Mathematical & Physical Sciences / Biological Sciences / Medicine, Dentistry & Pharmacy

Keywords

• Decision-making
• Learning theory, Information geometry
• Reinforcement learning
• Social intelligence
• Neural computation

Selected Publications

1. Fukuda H, Ma N, Suzuki S, Harasawa N, Ueno K, Gardner JL, Ichinohe N, Haruno M, Cheng K, Nakahara H.:
   "Computing Social Value Conversion in the Human Brain."
   Journal of Neuroscience. 39(26):5153-5172. (2019)
   10.1523/JNEUROSCI.3117-18.2019
2. Terada S, Sakurai Y, Nakahara H, Fujisawa S.:
   "Temporal and rate coding for discrete event sequences in the hippocampus."
   Neuron, 94, 1248-1262 (2017)
   10.1016/j.neuron.2017.05.024
3. Nakahara, H.:
   "Multiplexing signals in reinforcement learning with internal models and dopamine."
   Curr Opin Neurobiol, 25, 123-129 (2014)
   10.1016/j.conb.2014.01.001
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), 1125-1137 (2012)
   10.1016/j.neuron.2012.04.030
5. 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)
   10.1016/j.neuron.2010.06.031
6. Nakahara, H., and Kaveri, S.:
   "Internal-Time Temporal Difference Model for Neural Value-based Decision Making.", Neural Comput, 22(12), 3062-3106 (2010)
   10.1162/NECO_a_00049
7. Santos, GS., Gireesh, ED., Plenz, D., and Nakahara, H.:
   "Hierarchical interaction structre of neural activities in cortical slice cultures."
   J Neurosci, 30(26), 8720-8733 (2010)
   10.1523/JNEUROSCI.6141-09.2010
8. Nakahara, H., Itoh, H., Kawagoe, R., Takikawa, Y., Hikosaka, O.:
   "Dopamine neurons can represent context-dependent prediction error."
   Neuron, 41(2), 269-280 (2004)
   10.1016/S0896-6273(03)00869-9
9. Nakahara, H., and Amari, S.:
   "Information geometric measure for neural spikes."
   Neural Comput, 14(10), 2269-2316 (2002)
   10.1162/08997660260293238
10. Nakahara, H., Doya, K., and Hikosaka, O.:
    "Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuomotor sequences: A computational approach."
    J Cognitive Neurosci, 13(5), 626-647 (2001)
    10.1162/089892901750363208