RisingMid-Career

Friedemann Zenke

Friedrich Miescher Institute

ORCID OpenAlex

Core Metrics

Total Citations

7,035

H-Index

Publications

i10-Index

2-Year Citedness

3.2

avg citations per work

Ability Dimensions

Overall Score42

Impact33%

7,035 citations, h=27

Momentum36%

2yr mean: 3.2

Output29%

89 papers (5.2/year)

Efficiency58%

79 cites/paper

Novelty22%

3 unique research topics

Breadth40%

4 topic areas

Peak Power74%

Top 3 papers: 45% of citations

* Percentile scores are calculated relative to all scholars in the computational neuroscience dataset. Tags are assigned based on dimension combinations. Hover over the radar chart for details.

Scholar Profile Analysis

Friedemann Zenke is a rising scholar with 5k+ citations in computational neuroscience, currently affiliated with Friedrich Miescher Institute.

Over a 17-year academic career, published 89 papers (averaging 5.2 per year), with 7,035 citations.

Primary research areas include Finance, Finance, Signal processing.

Key Findings

Signature Work

"Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks" is the most influential work, with 1,181 citations, published in 2019.

Early Career Analysis (First 5 Years)

Career Start

2008 - 2012

Early Citations

953

Early Works

Early Impact %

13.5%

Top Early Career Paper

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Publication Timeline

Research Topics

Finance0%

Finance5.9%

Signal processing46.9%

Gene10.1%

Top Publications

12019• IEEE Signal Processing Magazine

Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks

1,181

Citations

22019• Nature Neuroscience

A deep learning framework for neuroscience

1,018

Citations

32017• PubMed

Continual Learning Through Synaptic Intelligence.

975

Citations

2011• Science

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

829

Citations

2015• Nature Communications

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

394

Citations

2021• Nature Neuroscience

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

234

Citations

2020• IEEE Transactions on Neural Networks and Learning Systems

The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks

221

Citations

2017• Philosophical Transactions of the Royal Society B Biological Sciences

Hebbian plasticity requires compensatory processes on multiple timescales

211

Citations

2017• Current Opinion in Neurobiology

The temporal paradox of Hebbian learning and homeostatic plasticity

203

Citations

2021• Neural Computation

The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks

201

Citations