Julian Göltz

About Julian Göltz

Julian Göltz, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at Ruprecht-Karls-Universität Heidelberg, specializes in the field of Computational Neuroscience, Machine Learning, Brain-Inspired Computing, Deep Learning, Theoretical Neuroscience.

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

Lu. i--A low-cost electronic neuron for education and outreach

Gradient-based methods for spiking physical systems

A scalable approach to modeling on accelerated neuromorphic hardware

The yin-yang dataset

Fast and energy-efficient neuromorphic deep learning with first-spike times

Visualizing a joint future of neuroscience and neuromorphic engineering

Fast and Energy-efficient deep Neuromorphic Learning

Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate

Julian Göltz Information

University

Position

PhD Student

Citations(all)

274

Citations(since 2020)

273

Cited By

40

hIndex(all)

6

hIndex(since 2020)

6

i10Index(all)

6

i10Index(since 2020)

6

Email

University Profile Page

Google Scholar

Julian Göltz Skills & Research Interests

Computational Neuroscience

Machine Learning

Brain-Inspired Computing

Deep Learning

Theoretical Neuroscience

Top articles of Julian Göltz

Lu. i--A low-cost electronic neuron for education and outreach

arXiv preprint arXiv:2404.16664

2024/4/25

Gradient-based methods for spiking physical systems

arXiv preprint arXiv:2309.10823

2023/8/29

The yin-yang dataset

2022/3/28

Laura Kriener
Laura Kriener

H-Index: 4

Julian Göltz
Julian Göltz

H-Index: 2

Fast and energy-efficient neuromorphic deep learning with first-spike times

Nature machine intelligence

2021/9

Visualizing a joint future of neuroscience and neuromorphic engineering

Neuron

2021/2/17

Fast and Energy-efficient deep Neuromorphic Learning

Brain-inspired Computing

2021

See List of Professors in Julian Göltz University(Ruprecht-Karls-Universität Heidelberg)

Co-Authors

academic-engine