한빛사논문
HyungGoo R. Kim1,9,10,*, Athar N. Malik1,2,9, John G. Mikhael3,4, Pol Bech1, Iku Tsutsui-Kimura1, Fangmiao Sun6,7,8, Yajun Zhang6,7,8, Yulong Li6,7,8, Mitsuko Watabe-Uchida1, Samuel J. Gershman5, Naoshige Uchida1,*
1Center for Brain Science, Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
2Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
3Program in Neuroscience, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
4MD-PhD Program, Harvard Medical School, 260 Longwood Avenue, Boston, MA 02115, USA
5Department of Psychology, Center for Brain Science, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
6State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing 100871, China
7Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
8PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
9These authors contributed equally
10Lead Contact
*Corresponding author
Abstract
Rapid phasic activity of midbrain dopamine neurons is thought to signal reward prediction errors (RPEs), resembling temporal difference errors used in machine learning. However, recent studies describing slowly increasing dopamine signals have instead proposed that they represent state values and arise independent from somatic spiking activity. Here we developed experimental paradigms using virtual reality that disambiguate RPEs from values. We examined dopamine circuit activity at various stages, including somatic spiking, calcium signals at somata and axons, and striatal dopamine concentrations. Our results demonstrate that ramping dopamine signals are consistent with RPEs rather than value, and this ramping is observed at all stages examined. Ramping dopamine signals can be driven by a dynamic stimulus that indicates a gradual approach to a reward. We provide a unified computational understanding of rapid phasic and slowly ramping dopamine signals: dopamine neurons perform a derivative-like computation over values on a moment-by-moment basis.
논문정보
관련 링크
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기