Adam Santoro

6.0k total citations · 2 hit papers
19 papers, 1.8k citations indexed

About

Adam Santoro is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Adam Santoro has authored 19 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 9 papers in Cognitive Neuroscience and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Adam Santoro's work include Memory and Neural Mechanisms (6 papers), Neural dynamics and brain function (5 papers) and Neural Networks and Applications (5 papers). Adam Santoro is often cited by papers focused on Memory and Neural Mechanisms (6 papers), Neural dynamics and brain function (5 papers) and Neural Networks and Applications (5 papers). Adam Santoro collaborates with scholars based in Canada, United States and United Kingdom. Adam Santoro's co-authors include Timothy Lillicrap, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Luke Marris, Geoffrey E. Hinton, Colin J. Akerman, Paul W. Frankland, Sheena A. Josselyn and Blake A. Richards and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neuron and Journal of Neuroscience.

In The Last Decade

Adam Santoro

19 papers receiving 1.7k citations

Hit Papers

Meta-learning with memory-augmented neural networks 2016 2026 2019 2022 2016 2020 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Meta-learning with memory-augmented neural networks
    2016 657 citations Adam Santoro, Sergey Bartunov et al. International Conference on Machine Learning profile →
  2. Backpropagation and the brain
    2020 432 citations Timothy Lillicrap, Adam Santoro et al. Nature reviews. Neuroscience profile →

Peers

Adam Santoro
Günther Palm Germany
Fabian H. Sinz Germany
Daniel Soudry Israel
Yi Zeng China
Ali A. Minai United States
Jonathan Tapson Australia
Ari S. Morcos United States
Jochen Triesch Germany
Sio-Hoï Ieng France
Kazushi Ikeda Japan
Günther Palm Germany
Adam Santoro
Citations per year, relative to Adam Santoro Adam Santoro (= 1×) peers Günther Palm

Countries citing papers authored by Adam Santoro

Since Specialization
Citations

This map shows the geographic impact of Adam Santoro's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Adam Santoro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam Santoro more than expected).

Fields of papers citing papers by Adam Santoro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adam Santoro. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Adam Santoro. The network helps show where Adam Santoro may publish in the future.

Co-authorship network of co-authors of Adam Santoro

This figure shows the co-authorship network connecting the top 25 collaborators of Adam Santoro. A scholar is included among the top collaborators of Adam Santoro based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Adam Santoro. Adam Santoro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Tran, Lina, Adam Santoro, Lulu Liu, et al.. (2022). Adult neurogenesis acts as a neural regularizer. Proceedings of the National Academy of Sciences. 119(45). e2206704119–e2206704119. 11 indexed citations
2.
Lillicrap, Timothy, Adam Santoro, Luke Marris, Colin J. Akerman, & Geoffrey E. Hinton. (2020). Backpropagation and the brain. Nature reviews. Neuroscience. 21(6). 335–346. 432 indexed citations breakdown →
3.
Ding, Xin, Felix Hill, Adam Santoro, & Matt Botvinick. (2020). Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures.. arXiv (Cornell University). 9 indexed citations
4.
Hill, Felix, Andrew K. Lampinen, Rosalia Schneider, et al.. (2020). Environmental drivers of systematicity and generalization in a situated agent.. International Conference on Learning Representations. 9 indexed citations
5.
Lampinen, Andrew K., Sébastien Racanière, Adam Santoro, et al.. (2020). Automated curriculum generation through setter-solver interactions. International Conference on Learning Representations. 7 indexed citations
6.
Hill, Felix, Adam Santoro, David G. T. Barrett, Ari S. Morcos, & Timothy Lillicrap. (2019). Learning to Make Analogies by Contrasting Abstract Relational Structure. International Conference on Learning Representations. 13 indexed citations
7.
Hill, Felix, Andrew K. Lampinen, Rosalia Schneider, et al.. (2019). Emergent Systematic Generalization In a Situated Agent. arXiv (Cornell University). 8 indexed citations
8.
Lillicrap, Timothy & Adam Santoro. (2019). Backpropagation through time and the brain. Current Opinion in Neurobiology. 55. 82–89. 59 indexed citations
9.
Xia, Frances, Axel Guskjolen, Adam I. Ramsaran, et al.. (2018). Elevation of Hippocampal Neurogenesis Induces a Temporally Graded Pattern of Forgetting of Contextual Fear Memories. Journal of Neuroscience. 38(13). 3190–3198. 64 indexed citations
10.
Zambaldi, Vinícius, David Raposo, Adam Santoro, et al.. (2018). Deep reinforcement learning with relational inductive biases. International Conference on Learning Representations. 47 indexed citations
11.
Bartunov, Sergey, Adam Santoro, Blake A. Richards, et al.. (2018). Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. arXiv (Cornell University). 31. 9368–9378. 37 indexed citations
12.
Barrett, David G. T., Felix Hill, Adam Santoro, Ari S. Morcos, & Timothy Lillicrap. (2018). Measuring abstract reasoning in neural networks. arXiv (Cornell University). 511–520. 55 indexed citations
13.
Raposo, David, Adam Santoro, David G. T. Barrett, et al.. (2017). Discovering objects and their relations from entangled scene representations. arXiv (Cornell University). 13 indexed citations
14.
Santoro, Adam, Paul W. Frankland, & Blake A. Richards. (2016). Memory Transformation Enhances Reinforcement Learning in Dynamic Environments. Journal of Neuroscience. 36(48). 12228–12242. 12 indexed citations
15.
Santoro, Adam, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, & Timothy Lillicrap. (2016). Meta-learning with memory-augmented neural networks. International Conference on Machine Learning. 1842–1850. 657 indexed citations breakdown →
16.
Berndt, A., Soo Yeun Lee, Jonas Wietek, et al.. (2015). Structural foundations of optogenetics: Determinants of channelrhodopsin ion selectivity. Proceedings of the National Academy of Sciences. 113(4). 822–829. 158 indexed citations
17.
Richards, Blake A., Frances Xia, Adam Santoro, et al.. (2014). Patterns across multiple memories are identified over time. Nature Neuroscience. 17(7). 981–986. 99 indexed citations
18.
Santoro, Adam & Paul W. Frankland. (2014). Chasing the Trace. Neuron. 84(2). 243–246. 5 indexed citations
19.
Santoro, Adam. (2013). Reassessing pattern separation in the dentate gyrus. Frontiers in Behavioral Neuroscience. 7. 96–96. 73 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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