NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping
Weijie Zheng(Anhui Jianzhu University), Hao Wang(Tongji University), Guo‐Qiang Bi(Chinese Academy of Sciences), Huawei Mu(University of Science and Technology of China), Jing Qi(Institute of Information Engineering), Yuxiao Cheng(University of Science and Technology of China), Jiajun Liu(University of Science and Technology of China), Zhiyi Chen(Central South University), Feng Wu(University of Science and Technology of China), Pak-Ming Lau(University of Science and Technology of China), Jin Tang(Anhui Jianzhu University), Debin Xia(Harbin Institute of Technology)
Cited by 11
Related Papers
Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges
|Advanced Materials|2019|830
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
|arXiv (Cornell University)|2017|615
Mesocorticolimbic Glutamatergic Pathway
|Journal of Neuroscience|2011|341
High-throughput mapping of a whole rhesus monkey brain at micrometer resolution
|Nature Biotechnology|2021|149