Brain-inspired learning in artificial neural networks: a review. Schmidgall, S., Achterberg, J., Miconi, T., Kirsch, L., Ziaei, R., Hajiseyedrazi, S. P., & Eshraghian, J. May, 2023. 1 citations (Semantic Scholar/arXiv) [2023-07-25] arXiv:2305.11252 [cs, q-bio]![Brain-inspired learning in artificial neural networks: a review [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to enhance these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. Ultimately, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.
@misc{schmidgall_brain-inspired_2023,
title = {Brain-inspired learning in artificial neural networks: a review},
shorttitle = {Brain-inspired learning in artificial neural networks},
url = {http://arxiv.org/abs/2305.11252},
abstract = {Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to enhance these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. Ultimately, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.},
language = {en},
urldate = {2023-07-24},
publisher = {arXiv},
author = {Schmidgall, Samuel and Achterberg, Jascha and Miconi, Thomas and Kirsch, Louis and Ziaei, Rojin and Hajiseyedrazi, S. Pardis and Eshraghian, Jason},
month = may,
year = {2023},
note = {1 citations (Semantic Scholar/arXiv) [2023-07-25]
arXiv:2305.11252 [cs, q-bio]},
keywords = {/unread, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition},
}
Downloads: 0
{"_id":"DiPyvGYzhYdLst8ay","bibbaseid":"schmidgall-achterberg-miconi-kirsch-ziaei-hajiseyedrazi-eshraghian-braininspiredlearninginartificialneuralnetworksareview-2023","author_short":["Schmidgall, S.","Achterberg, J.","Miconi, T.","Kirsch, L.","Ziaei, R.","Hajiseyedrazi, S. P.","Eshraghian, J."],"bibdata":{"bibtype":"misc","type":"misc","title":"Brain-inspired learning in artificial neural networks: a review","shorttitle":"Brain-inspired learning in artificial neural networks","url":"http://arxiv.org/abs/2305.11252","abstract":"Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to enhance these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. Ultimately, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.","language":"en","urldate":"2023-07-24","publisher":"arXiv","author":[{"propositions":[],"lastnames":["Schmidgall"],"firstnames":["Samuel"],"suffixes":[]},{"propositions":[],"lastnames":["Achterberg"],"firstnames":["Jascha"],"suffixes":[]},{"propositions":[],"lastnames":["Miconi"],"firstnames":["Thomas"],"suffixes":[]},{"propositions":[],"lastnames":["Kirsch"],"firstnames":["Louis"],"suffixes":[]},{"propositions":[],"lastnames":["Ziaei"],"firstnames":["Rojin"],"suffixes":[]},{"propositions":[],"lastnames":["Hajiseyedrazi"],"firstnames":["S.","Pardis"],"suffixes":[]},{"propositions":[],"lastnames":["Eshraghian"],"firstnames":["Jason"],"suffixes":[]}],"month":"May","year":"2023","note":"1 citations (Semantic Scholar/arXiv) [2023-07-25] arXiv:2305.11252 [cs, q-bio]","keywords":"/unread, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition","bibtex":"@misc{schmidgall_brain-inspired_2023,\n\ttitle = {Brain-inspired learning in artificial neural networks: a review},\n\tshorttitle = {Brain-inspired learning in artificial neural networks},\n\turl = {http://arxiv.org/abs/2305.11252},\n\tabstract = {Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to enhance these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. Ultimately, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.},\n\tlanguage = {en},\n\turldate = {2023-07-24},\n\tpublisher = {arXiv},\n\tauthor = {Schmidgall, Samuel and Achterberg, Jascha and Miconi, Thomas and Kirsch, Louis and Ziaei, Rojin and Hajiseyedrazi, S. Pardis and Eshraghian, Jason},\n\tmonth = may,\n\tyear = {2023},\n\tnote = {1 citations (Semantic Scholar/arXiv) [2023-07-25]\narXiv:2305.11252 [cs, q-bio]},\n\tkeywords = {/unread, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition},\n}\n\n","author_short":["Schmidgall, S.","Achterberg, J.","Miconi, T.","Kirsch, L.","Ziaei, R.","Hajiseyedrazi, S. P.","Eshraghian, J."],"key":"schmidgall_brain-inspired_2023","id":"schmidgall_brain-inspired_2023","bibbaseid":"schmidgall-achterberg-miconi-kirsch-ziaei-hajiseyedrazi-eshraghian-braininspiredlearninginartificialneuralnetworksareview-2023","role":"author","urls":{"Paper":"http://arxiv.org/abs/2305.11252"},"keyword":["/unread","Computer Science - Artificial Intelligence","Computer Science - Machine Learning","Computer Science - Neural and Evolutionary Computing","Quantitative Biology - Neurons and Cognition"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"misc","biburl":"https://bibbase.org/zotero/victorjhu","dataSources":["CmHEoydhafhbkXXt5"],"keywords":["/unread","computer science - artificial intelligence","computer science - machine learning","computer science - neural and evolutionary computing","quantitative biology - neurons and cognition"],"search_terms":["brain","inspired","learning","artificial","neural","networks","review","schmidgall","achterberg","miconi","kirsch","ziaei","hajiseyedrazi","eshraghian"],"title":"Brain-inspired learning in artificial neural networks: a review","year":2023}