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EmergingEarly-Career

Benjamin Scellier

Independent

OpenAlex

Core Metrics

Total Citations

1,642

H-Index

9

Publications

28

i10-Index

9

2-Year Citedness

2.9

avg citations per work

Ability Dimensions

Overall Score33
Landmark Paper
Impact4%

1,642 citations, h=9

Momentum31%

2yr mean: 2.9

Output6%

28 papers (3.1/year)

Efficiency39%

59 cites/paper

Novelty5%

2 unique research topics

Breadth50%

5 topic areas

Peak Power97%

Top 3 papers: 90% 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

Benjamin Scellier is a emerging scholar in computational neuroscience, currently affiliated with Independent Research.

Over a 9-year academic career, published 28 papers (averaging 3.1 per year), with 1,642 citations.

Outstanding early career performance: first 5 years account for 96.9% of total citations (1,591), showing strong early impact.

Primary research areas include Statistics, Statistics, Statistics.

Key Findings

Signature Work

"A deep learning framework for neuroscience" is the most influential work, with 1,018 citations, published in 2019.

Early Breakthrough

Over half of citations come from the first 5 years, possibly from pioneering work or classic textbooks.

Early Career Analysis (First 5 Years)

Career Start

2016 - 2020

Early Citations

1,591

Early Works

16

Early Impact %

96.9%

Top Early Career Paper

A deep learning framework for neuroscience

Publication Timeline

Research Topics

Statistics0%
Statistics7%
Statistics10.3%
Deep learning56.1%
Deep learning42.8%

Top Publications

12019• Nature Neuroscience

A deep learning framework for neuroscience

1,018

Citations

22017• Frontiers in Computational Neuroscience

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

414

Citations

32020• arXiv (Cornell University)

Training End-to-End Analog Neural Networks with Equilibrium Propagation

38

Citations

2016• arXiv (Cornell University)

Towards a Biologically Plausible Backprop

31

Citations

2018• arXiv (Cornell University)

Generalization of Equilibrium Propagation to Vector Field Dynamics

19

Citations

2016• arXiv (Cornell University)

Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible

18

Citations

2020• arXiv (Cornell University)

Equilibrium Propagation with Continual Weight Updates

17

Citations

2016• arXiv (Cornell University)

Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation

15

Citations

2021• Frontiers in Neuroscience

Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing Its Gradient Estimator Bias

10

Citations

2019• arXiv (Cornell University)

Updates of Equilibrium Prop Match Gradients of Backprop Through Time in\n an RNN with Static Input

8

Citations