Evolutionary learning in neural networks by heterosynaptic plasticity
Summary: Training biophysical neuron models provides insights into brain circuits’ organization and problem-solving capabilities. Traditional training methods like backpropagation face challenges with complex models due to instability and gradient issues. We explore evolutionary algorithms (EAs) com...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225006017 |
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| author | Zedong Bi Ruiqi Fu Guozhang Chen Dongping Yang Yu Zhou Liang Tian |
| author_facet | Zedong Bi Ruiqi Fu Guozhang Chen Dongping Yang Yu Zhou Liang Tian |
| author_sort | Zedong Bi |
| collection | DOAJ |
| description | Summary: Training biophysical neuron models provides insights into brain circuits’ organization and problem-solving capabilities. Traditional training methods like backpropagation face challenges with complex models due to instability and gradient issues. We explore evolutionary algorithms (EAs) combined with heterosynaptic plasticity as a gradient-free alternative. Our EA models agents with distinct neuron information routes, evaluated via alternating gating, and guided by dopamine-driven plasticity. This model draws inspiration from various biological mechanisms, such as dopamine function, dendritic spine meta-plasticity, memory replay, and cooperative synaptic plasticity within dendritic neighborhoods. Neural networks trained with this model recapitulate brain-like dynamics during cognition. Our method effectively trains spiking and analog neural networks in both feedforward and recurrent architectures, it also achieves performance in tasks like MNIST classification and Atari games comparable to gradient-based methods. Overall, this research extends training approaches for biophysical neuron models, offering a robust alternative to traditional algorithms. |
| format | Article |
| id | doaj-art-e0f7836a4e9b4de0808e96f290e6d02a |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-e0f7836a4e9b4de0808e96f290e6d02a2025-08-20T03:18:19ZengElsevieriScience2589-00422025-05-0128511234010.1016/j.isci.2025.112340Evolutionary learning in neural networks by heterosynaptic plasticityZedong Bi0Ruiqi Fu1Guozhang Chen2Dongping Yang3Yu Zhou4Liang Tian5Lingang Laboratory, Shanghai 200031, China; Corresponding authorDepartment of Physics, Hong Kong Baptist University, Hong Kong, ChinaNational Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, ChinaResearch Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou 311121, ChinaSchool of Life Sciences and Health, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266011, ChinaDepartment of Physics, Hong Kong Baptist University, Hong Kong, China; Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, China; Institute of Systems Medicine and Health Sciences, Hong Kong Baptist University, Hong Kong, China; Corresponding authorSummary: Training biophysical neuron models provides insights into brain circuits’ organization and problem-solving capabilities. Traditional training methods like backpropagation face challenges with complex models due to instability and gradient issues. We explore evolutionary algorithms (EAs) combined with heterosynaptic plasticity as a gradient-free alternative. Our EA models agents with distinct neuron information routes, evaluated via alternating gating, and guided by dopamine-driven plasticity. This model draws inspiration from various biological mechanisms, such as dopamine function, dendritic spine meta-plasticity, memory replay, and cooperative synaptic plasticity within dendritic neighborhoods. Neural networks trained with this model recapitulate brain-like dynamics during cognition. Our method effectively trains spiking and analog neural networks in both feedforward and recurrent architectures, it also achieves performance in tasks like MNIST classification and Atari games comparable to gradient-based methods. Overall, this research extends training approaches for biophysical neuron models, offering a robust alternative to traditional algorithms.http://www.sciencedirect.com/science/article/pii/S2589004225006017Biological sciencesNeuroscienceBiophysics |
| spellingShingle | Zedong Bi Ruiqi Fu Guozhang Chen Dongping Yang Yu Zhou Liang Tian Evolutionary learning in neural networks by heterosynaptic plasticity iScience Biological sciences Neuroscience Biophysics |
| title | Evolutionary learning in neural networks by heterosynaptic plasticity |
| title_full | Evolutionary learning in neural networks by heterosynaptic plasticity |
| title_fullStr | Evolutionary learning in neural networks by heterosynaptic plasticity |
| title_full_unstemmed | Evolutionary learning in neural networks by heterosynaptic plasticity |
| title_short | Evolutionary learning in neural networks by heterosynaptic plasticity |
| title_sort | evolutionary learning in neural networks by heterosynaptic plasticity |
| topic | Biological sciences Neuroscience Biophysics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225006017 |
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