Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network
Causal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas. Neural assembles participate in Bayesian inference. However, the contributions of neural ass...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10649571/ |
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| author | Weisi Liu Xiaogang Pan |
| author_facet | Weisi Liu Xiaogang Pan |
| author_sort | Weisi Liu |
| collection | DOAJ |
| description | Causal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas. Neural assembles participate in Bayesian inference. However, the contributions of neural assemblies in causal inference remain unclear. In this paper, a Bayesian spiking neural network is designed with the entropy-maximization (EM) method to simulate causal inference of visual hidden cues. Hidden cues determine types of visual images in simulations. With images received, the network generates neural spiking trains and modifies its plastic weights with the EM method with constraint conditions. After modifications, the network can identify hidden cues with induced neural responses. Over repeated simulations, similarity and responsivity of neural activities are measured to determine neural assembles. Through principal component analysis of neural responses, contributions of neural assembles in causal inference are explored. During identifications of given stimuli, different neural assemblies make various time-varying contributions. With acceptable performance in causal inference of designed stimuli, the network simulates the emergence of neural assembles and measures their contributions. The Bayesian spiking neural network with the EM method provides the possible framework to explore effects of neural assembles in cause inference. |
| format | Article |
| id | doaj-art-108e1f874c1549fe8a66833b605c2c2c |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-108e1f874c1549fe8a66833b605c2c2c2025-08-20T01:59:09ZengIEEEIEEE Access2169-35362024-01-011218444218445510.1109/ACCESS.2024.345055110649571Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural NetworkWeisi Liu0https://orcid.org/0000-0002-8264-9641Xiaogang Pan1School of Mathematics and Information Sciences, Yantai University, Yantai, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, ChinaCausal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas. Neural assembles participate in Bayesian inference. However, the contributions of neural assemblies in causal inference remain unclear. In this paper, a Bayesian spiking neural network is designed with the entropy-maximization (EM) method to simulate causal inference of visual hidden cues. Hidden cues determine types of visual images in simulations. With images received, the network generates neural spiking trains and modifies its plastic weights with the EM method with constraint conditions. After modifications, the network can identify hidden cues with induced neural responses. Over repeated simulations, similarity and responsivity of neural activities are measured to determine neural assembles. Through principal component analysis of neural responses, contributions of neural assembles in causal inference are explored. During identifications of given stimuli, different neural assemblies make various time-varying contributions. With acceptable performance in causal inference of designed stimuli, the network simulates the emergence of neural assembles and measures their contributions. The Bayesian spiking neural network with the EM method provides the possible framework to explore effects of neural assembles in cause inference.https://ieeexplore.ieee.org/document/10649571/Causal inferenceBayesian inferencespiking neural networkentropy-maximization method |
| spellingShingle | Weisi Liu Xiaogang Pan Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network IEEE Access Causal inference Bayesian inference spiking neural network entropy-maximization method |
| title | Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network |
| title_full | Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network |
| title_fullStr | Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network |
| title_full_unstemmed | Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network |
| title_short | Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network |
| title_sort | effects of neural assembles in causal inference based on an entropy maximization bayesian neural network |
| topic | Causal inference Bayesian inference spiking neural network entropy-maximization method |
| url | https://ieeexplore.ieee.org/document/10649571/ |
| work_keys_str_mv | AT weisiliu effectsofneuralassemblesincausalinferencebasedonanentropymaximizationbayesianneuralnetwork AT xiaogangpan effectsofneuralassemblesincausalinferencebasedonanentropymaximizationbayesianneuralnetwork |