Naoshige Uchida

Naoshige Uchida

Harvard University

H-index: 50

North America-United States

About Naoshige Uchida

Naoshige Uchida, With an exceptional h-index of 50 and a recent h-index of 44 (since 2020), a distinguished researcher at Harvard University, specializes in the field of Neurobiology, Decision Making, Reinforcement learning, Dopamine, Olfaction.

His recent articles reflect a diverse array of research interests and contributions to the field:

Glutamate inputs send prediction error of reward, but not negative value of aversive stimuli, to dopamine neurons

Interpretable deep learning for deconvolutional analysis of neural signals

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

An opponent striatal circuit for distributional reinforcement learning

Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model

Spontaneous behaviour is structured by reinforcement without explicit reward

Interpretable deep learning for deconvolution of multiplexed neural signals

Multi-timescale reinforcement learning in the brain

Naoshige Uchida Information

University

Position

Professor of Molecular and Cellular Biology

Citations(all)

15442

Citations(since 2020)

7746

Cited By

11007

hIndex(all)

50

hIndex(since 2020)

44

i10Index(all)

61

i10Index(since 2020)

55

Email

University Profile Page

Harvard University

Google Scholar

View Google Scholar Profile

Naoshige Uchida Skills & Research Interests

Neurobiology

Decision Making

Reinforcement learning

Dopamine

Olfaction

Top articles of Naoshige Uchida

Title

Journal

Author(s)

Publication Date

Glutamate inputs send prediction error of reward, but not negative value of aversive stimuli, to dopamine neurons

Neuron

Ryunosuke Amo

Naoshige Uchida

Mitsuko Watabe-Uchida

2024/1/22

Interpretable deep learning for deconvolutional analysis of neural signals

bioRxiv

Bahareh Tolooshams

Sara Matias

Hao Wu

Simona Temereanca

Naoshige Uchida

...

2024/1/6

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

bioRxiv

Lechen Qian

Mark Burrell

Jay A Hennig

Sara Matias

Venkatesh N Murthy

...

2024

An opponent striatal circuit for distributional reinforcement learning

bioRxiv

Adam S Lowet

Qiao Zheng

Melissa Meng

Sara Matias

Jan Drugowitsch

...

2024

Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model

bioRxiv

Sandra Romero Pinto

Naoshige Uchida

2023/11/14

Spontaneous behaviour is structured by reinforcement without explicit reward

Nature

Jeffrey E Markowitz

Winthrop F Gillis

Maya Jay

Jeffrey Wood

Ryley W Harris

...

2023/2/2

Interpretable deep learning for deconvolution of multiplexed neural signals

Computational and Systems Neuroscience Abstracts

Bahareh Tolooshams

Sara Matias

Hao Wu

Naoshige Uchida

Venkatesh N Murthy

...

2023

Multi-timescale reinforcement learning in the brain

bioRxiv

Paul Masset

Pablo Tano

HyungGoo R Kim

Athar N Malik

Alexandre Pouget

...

2023/11/14

Emergence of belief-like representations through reinforcement learning

PLOS Computational Biology

Jay A Hennig

Sandra A Romero Pinto

Takahiro Yamaguchi

Scott W Linderman

Naoshige Uchida

...

2023/9/11

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics

bioRxiv

Michael Bukwich

Malcolm G Campbell

David Zoltowski

Lyle Kingsbury

Momchil S Tomov

...

2023/9/5

A hypothalamic circuit underlying the dynamic control of social homeostasis

BioRxiv

Ding Liu

Mostafizur Rahman

Autumn Johnson

Iku Tsutsui-Kimura

Nicolai Pena

...

2023/5/19

Distributional Reinforcement Learning in the Mammalian Brain

Adam S Lowet

Qiao Zheng

Melissa Meng

Sara Matias

Jan Drugowitsch

...

2023/12/18

Dynamical management of potential threats regulated by dopamine and direct-and indirect-pathway neurons in the tail of the striatum

bioRxiv

Iku Tsutsui-Kimura

Naoshige Uchida

Mitsuko Watabe-Uchida

2022/2/7

Unsupervised sparse deconvolutional learning of features driving neural activity

Computational and Systems Neuroscience Abstracts

Bahareh Tolooshams

Hao Wu

Naoshige Uchida

Venkatesh N Murthy

Paul Masset

...

2022

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Neuron

Korleki Akiti

Iku Tsutsui-Kimura

Yudi Xie

Alexander Mathis

Jeffrey E Markowitz

...

2022/11/16

A gradual temporal shift of dopamine responses mirrors the progression of temporal difference error in machine learning

Nature neuroscience

Ryunosuke Amo

Sara Matias

Akihiro Yamanaka

Kenji F Tanaka

Naoshige Uchida

...

2022/8

The role of state uncertainty in the dynamics of dopamine

Current Biology

John G Mikhael

HyungGoo R Kim

Naoshige Uchida

Samuel J Gershman

2022/3/14

Dopamine signals as temporal difference errors: recent advances

Clara Kwon Starkweather

Naoshige Uchida

2021/4/1

Distributional reinforcement learning in the brain

Adam S Lowet

Qiao Zheng

Sara Matias

Jan Drugowitsch

Naoshige Uchida

2020/12/1

Dopamine Reward Prediction Errors: The Interplay between Experiments and Theory

Clara K Starkweather

Naoshige Uchida

2020/5/12

See List of Professors in Naoshige Uchida University(Harvard University)

Co-Authors

H-index: 72
Samuel Gershman

Samuel Gershman

Harvard University

H-index: 68
ranulfo romo

ranulfo romo

Universidad Nacional Autónoma de México

H-index: 68
Markus Meister

Markus Meister

California Institute of Technology

H-index: 48
Venkatesh N. Murthy

Venkatesh N. Murthy

Harvard University

H-index: 23
Mitsuko Watabe-Uchida

Mitsuko Watabe-Uchida

Harvard University

H-index: 23
Mackenzie Weygandt Mathis

Mackenzie Weygandt Mathis

École Polytechnique Fédérale de Lausanne

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