In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.
James, Conrad D., et al. "A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications." Biologically Inspired Cognitive Architectures, vol. 19, no. C, Jan. 2017. https://doi.org/10.1016/j.bica.2016.11.002
James, Conrad D., Aimone, James B., Miner, Nadine E., Vineyard, Craig M., Rothganger, Fredrick H., Carlson, Kristofor D., Mulder, Samuel A., Draelos, Timothy J., Faust, Aleksandra, Marinella, Matthew J., Naegle, John H., & Plimpton, Steven J. (2017). A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications. Biologically Inspired Cognitive Architectures, 19(C). https://doi.org/10.1016/j.bica.2016.11.002
James, Conrad D., Aimone, James B., Miner, Nadine E., et al., "A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications," Biologically Inspired Cognitive Architectures 19, no. C (2017), https://doi.org/10.1016/j.bica.2016.11.002
@article{osti_1340263,
author = {James, Conrad D. and Aimone, James B. and Miner, Nadine E. and Vineyard, Craig M. and Rothganger, Fredrick H. and Carlson, Kristofor D. and Mulder, Samuel A. and Draelos, Timothy J. and Faust, Aleksandra and Marinella, Matthew J. and others},
title = {A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications},
annote = {In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.},
doi = {10.1016/j.bica.2016.11.002},
url = {https://www.osti.gov/biblio/1340263},
journal = {Biologically Inspired Cognitive Architectures},
issn = {ISSN 2212-683X},
number = {C},
volume = {19},
place = {United States},
publisher = {Elsevier},
year = {2017},
month = {01}}
2015 IEEE International Conference on Semantic Computing (ICSC), Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)https://doi.org/10.1109/ICOSC.2015.7050812
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millenniumhttps://doi.org/10.1109/IJCNN.2000.861291
Schemmel, Johannes; Briiderle, Daniel; Griibl, Andreas
2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systemshttps://doi.org/10.1109/ISCAS.2010.5536970