EmergingMid-Career

Grace W. Lindsay

New York University

ORCID OpenAlex

Core Metrics

Total Citations

2,930

H-Index

Publications

i10-Index

2-Year Citedness

36.1

avg citations per work

Ability Dimensions

Overall Score46

Dark Horse

Landmark Paper

Impact6%

2,930 citations, h=13

Momentum93%

2yr mean: 36.1

Output7%

34 papers (3.1/year)

Efficiency63%

86 cites/paper

Novelty22%

3 unique research topics

Breadth40%

4 topic areas

Peak Power93%

Top 3 papers: 66% 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

Grace W. Lindsay is a emerging scholar in computational neuroscience, currently affiliated with New York University.

Over a 11-year academic career, published 34 papers (averaging 3.1 per year), with 2,930 citations.

Primary research areas include Similarity (geometry), Statistics, Statistics.

Key Findings

Signature Work

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

Sustained Impact

Two-year mean citedness of 36.1 indicates research continues to generate significant impact.

Early Career Analysis (First 5 Years)

Career Start

2014 - 2018

Early Citations

598

Early Works

Early Impact %

20.4%

Top Early Career Paper

Parallel processing by cortical inhibition enables context-dependent behavior

Publication Timeline

Research Topics

Similarity (geometry)45.7%

Statistics0%

Deep learning48.1%

Top Publications

12019• Nature Neuroscience

A deep learning framework for neuroscience

1,018

Citations

22020• Journal of Cognitive Neuroscience

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

525

Citations

32016• Nature Neuroscience

Parallel processing by cortical inhibition enables context-dependent behavior

402

Citations

2020• Frontiers in Computational Neuroscience

Attention in Psychology, Neuroscience, and Machine Learning

281

Citations

2023• Nature reviews. Neuroscience

The neuroconnectionist research programme

195

Citations

2023• arXiv (Cornell University)

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

178

Citations

2018• eLife

How biological attention mechanisms improve task performance in a large-scale visual system model

Citations

2017• Journal of Neuroscience

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

Citations

2021• PubMed

Neuromatch Academy: Teaching Computational Neuroscience with Global Accessibility.

Citations

2017• bioRxiv (Cold Spring Harbor Laboratory)

Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal Cortex

Citations