Question 1

Which countries lead Neural Networks and Reservoir Computing research?

Accepted Answer

By volume, China and the United States publish the most (2.7k and 1.4k works in 2022-now).

Question 2

Which institutions lead Neural Networks and Reservoir Computing research?

Accepted Answer

By volume in 2022-now, Chinese Academy of Sciences publishes the most Neural Networks and Reservoir Computing research, followed by Centre National de la Recherche Scientifique and Beijing University of Posts and Telecommunications.

Question 3

Who are the leading researchers in Neural Networks and Reservoir Computing?

Accepted Answer

The most-cited researchers publishing on Neural Networks and Reservoir Computing include Zhong Lin Wang, Yoshua Bengio and Geoffrey E. Hinton.

Question 4

Where is Neural Networks and Reservoir Computing research done?

Accepted Answer

The largest centres of Neural Networks and Reservoir Computing research in 2022-now are Beijing (China), Shanghai (China), Tokyo (Japan) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Palma and Eindhoven.

Question 5

Where is the best place to study Neural Networks and Reservoir Computing?

Accepted Answer

Among universities, judged by research, Dongguk University, National University of Singapore and Tsinghua University 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.

Question 6

Is Neural Networks and Reservoir Computing research growing?

Accepted Answer

Output in 2022-2025 was 99% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Neural Networks and Reservoir Computing.

Question 7

What are the most cited papers in Neural Networks and Reservoir Computing?

Accepted Answer

The most cited paper in Neural Networks and Reservoir Computing is "A scheme for efficient quantum computation with linear optics" (E. Knill, Raymond Laflamme, Gerard J Milburn, 2001), with 5,952 citations. Next come "A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures" (5,666) and "Deep Sparse Rectifier Neural Networks" (5,420).