Jonathan Kao

Jonathan Kao

University of California, Los Angeles

H-index: 26

North America-United States

About Jonathan Kao

Jonathan Kao, With an exceptional h-index of 26 and a recent h-index of 25 (since 2020), a distinguished researcher at University of California, Los Angeles, specializes in the field of Neuroscience, Neural Engineering, Statistical Signal Processing, Machine Learning.

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

Control of feeding by a bottom-up midbrain-subthalamic pathway

Gacs-korner common information variational autoencoder

Spontaneous pain dynamics characterized by stochasticity in awake human LFP with chronic pain

A cortical information bottleneck during decision-making

Multiplicative Recurrent Neural Network for Fast and Robust Intracortical Brain Machine Interface Decoders

GABAergic CA1 neurons are more stable following context changes than glutamatergic cells

Learning rule influences recurrent network representations but not attractor structure in decision-making tasks

A mechanistic multi-area recurrent network model of decision-making

Jonathan Kao Information

University

Position

___

Citations(all)

3271

Citations(since 2020)

2253

Cited By

1890

hIndex(all)

26

hIndex(since 2020)

25

i10Index(all)

40

i10Index(since 2020)

37

Email

University Profile Page

Google Scholar

Jonathan Kao Skills & Research Interests

Neuroscience

Neural Engineering

Statistical Signal Processing

Machine Learning

Top articles of Jonathan Kao

Control of feeding by a bottom-up midbrain-subthalamic pathway

Nature Communications

2024/3/7

Gacs-korner common information variational autoencoder

Advances in Neural Information Processing Systems

2024/2/13

Spontaneous pain dynamics characterized by stochasticity in awake human LFP with chronic pain

bioRxiv

2024

A cortical information bottleneck during decision-making

bioRxiv

2023/7/14

Multiplicative Recurrent Neural Network for Fast and Robust Intracortical Brain Machine Interface Decoders

2019/3/5

GABAergic CA1 neurons are more stable following context changes than glutamatergic cells

Scientific reports

2022/6/20

Learning rule influences recurrent network representations but not attractor structure in decision-making tasks

Advances in Neural Information Processing Systems

2021/12/6

Michael Kleinman
Michael Kleinman

H-Index: 2

Jonathan Kao
Jonathan Kao

H-Index: 20

A mechanistic multi-area recurrent network model of decision-making

Advances in neural information processing systems

2021/12/6

Michael Kleinman
Michael Kleinman

H-Index: 2

Jonathan Kao
Jonathan Kao

H-Index: 20

Dorsal premammillary projection to periaqueductal gray controls escape vigor from innate and conditioned threats

eLife

2021/9/1

Redundant information neural estimation

Entropy

2021/7/20

Shared dorsal periaqueductal gray activation patterns during exposure to innate and conditioned threats

Journal of Neuroscience

2021/6/23

Coordination of escape and spatial navigation circuits orchestrates versatile flight from threats

Neuron

2021/6/2

An artificial intelligence that increases simulated brain–computer interface performance

Journal of Neural Engineering

2021/5/13

Dorsal periaqueductal gray ensembles represent approach and avoidance states

Elife

2021/5/6

Measurement, manipulation and modeling of brain-wide neural population dynamics

Nature communications

2021/1/27

A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces

Nature Biomedical Engineering

2020/10

Task-outcome error signals and their use in brain-machine interfaces

2020/9/22

Structure in neural activity during observed and executed movements is shared at the neural population level, not in single neurons

Cell reports

2020/8/11

Learning robust representations with score invariant learning

ICML UDL Workshop

2020

See List of Professors in Jonathan Kao University(University of California, Los Angeles)

Co-Authors

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