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Memory and Neural Mechanisms

Memory and Neural Mechanisms is a research topic within Cognitive Neuroscience. Science Explorer counts 44k research works in it since 1950. 23.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers explores the neural mechanisms underlying memory formation, consolidation, reconsolidation, and retrieval, with a focus on the role of the hippocampus, amygdala, and prefrontal cortex. It also delves into the neural basis of spatial navigation and the interplay between memory and navigation processes in the brain.

  • Memory
  • Hippocampus
  • Navigation
  • Amygdala
  • Neural Circuits
  • Fear Conditioning
  • Spatial Representation
  • Neurobiology
  • Reconsolidation
  • Prefrontal Cortex
Research works
44k
fractional, since 1950
In the world top 10%
11k
per year above
Top-10% rate
23.8%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-5%
the tick is no change

Which countries lead Memory and Neural Mechanisms research?

By volume, the United States and China publish the most (1.6k and 499 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 1.6k works
  2. 2 China 499 works
  3. 3 Germany 324 works
  4. 4 United Kingdom 299 works
  5. 5 Canada 256 works
  6. 6 Japan 202 works
  7. 7 France 170 works
  8. 8 Italy 127 works
  9. 9 Spain 101 works
  10. 10 Netherlands 95 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 44.0%China: 13.5%Germany: 8.8%United Kingdom: 8.1%6 others listed: 25.7%44%largest
United States1,628 · 44.0%China499 · 13.5%Germany324 · 8.8%United Kingdom299 · 8.1%6 others listed951 · 25.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Memory and Neural Mechanisms research?

By volume in 2022–2025, University of Toronto publishes the most Memory and Neural Mechanisms research, followed by University College London and Columbia University.

By volume, 2022–2025

  1. 1 University of TorontoCanada 37 works
  2. 2 University College LondonUnited Kingdom 33 works
  3. 3 Columbia UniversityUnited States 32 works
  4. 4 Harvard UniversityUnited States 29 works
  5. 5 University of California, IrvineUnited States 28 works
  6. 6 Yale UniversityUnited States 28 works
  7. 7 Ruhr University BochumGermany 26 works
  8. 8 University of California, Los AngelesUnited States 25 works
  9. 9 University of OxfordUnited Kingdom 24 works
  10. 10 University of California San DiegoUnited States 23 works

Who are the leading researchers in Memory and Neural Mechanisms?

The most-cited researchers publishing on Memory and Neural Mechanisms include Bruce S. McEwen.

  1. 1 Bruce S. McEwen United States 3.6k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Memory and Neural Mechanisms research done?

The largest centres of Memory and Neural Mechanisms research in 2022–2025 are Beijing (China), New York (United States), London (United Kingdom) and Toronto (Canada). Among places with at least 20 works in it, it is an unusually large share of all research in Bochum and Irvine.

Largest cities, 2022–2025

  1. 1 Beijing China 120 works
  2. 2 New York United States 105 works
  3. 3 London United Kingdom 87 works
  4. 4 Toronto Canada 78 works
  5. 5 Paris France 73 works
  6. 6 Boston United States 66 works
  7. 7 Tokyo Japan 59 works
  8. 8 Montreal Canada 56 works
  9. 9 Shanghai China 52 works
  10. 10 Moscow Russia 49 works

Where it is the local speciality

  1. BochumDE · 31.9 works13×
  2. IrvineUS · 31.1 works11×
← less than its size predictsmore →

Location quotient: how much more of its research is in Memory and Neural Mechanisms than the world average.

See Memory and Neural Mechanisms on the map

Where is the best place to study Memory and Neural Mechanisms?

Among universities, judged by research, Columbia University, University College London and University of Oregon score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 36.65%fractional works in this node (log) →share in the world top 10% →Columbia University: 32, 43.1%University College London: 33, 37.4%University of Oregon: 13, 38.5%New York University: 22, 47.6%Boston University: 22, 33.1%Weizmann Institute of Science: 9, 46.6%Princeton University: 15, 41.2%University of California, Irvine: 28, 24.6%Ruhr University Bochum: 26, 19.8%Harvard University: 29, 34.6%New York UniversityColumbia UniversityUniversity of OregonUniversity College L…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Columbia UniversityUnited States 70.643.1%8.7×32 -5.2%
2 University College LondonUnited Kingdom 63.037.4%5.3×33 +11.0%
3 University of OregonUnited States 61.638.5%20.1×13 +59.2%
4 New York UniversityUnited States 60.947.6%6.0×22 +3.3%
5 Boston UniversityUnited States 60.833.1%9.8×22 -25.3%
6 Weizmann Institute of ScienceIsrael 60.446.6%13.5×9 -35.8%
7 Princeton UniversityUnited States 59.841.2%7.5×15 +53.2%
8 University of California, IrvineUnited States 59.024.6%10.8×28 -23.2%
9 Ruhr University BochumGermany 57.919.8%14.7×26 +4.8%
10 Harvard UniversityUnited States 56.734.6%5.2×29 -7.2%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Memory and Neural Mechanisms research growing?

Output in 2018–2022 was 5% lower than in 2013–2017, peaking in 2013. The fastest-growing topics are Memory and Neural Mechanisms.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.