Spike-Timing-Dependent Plasticity for Bernoulli Message Passing
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912794902790144 |
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| author | Adamiat, Sepideh Kouw, Wouter M. de Vries, Bert |
| author_facet | Adamiat, Sepideh Kouw, Wouter M. de Vries, Bert |
| contents | Bayesian inference provides a principled framework for understanding brain function, while neural activity in the brain is inherently spike-based. This paper bridges these two perspectives by designing spiking neural networks that simulate Bayesian inference through message passing for Bernoulli messages. To train the networks, we employ spike-timing-dependent plasticity, a biologically plausible mechanism for synaptic plasticity which is based on the Hebbian rule. Our results demonstrate that the network's performance closely matches the true numerical solution. We further demonstrate the versatility of our approach by implementing a factor graph example from coding theory, illustrating signal transmission over an unreliable channel. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spike-Timing-Dependent Plasticity for Bernoulli Message Passing Adamiat, Sepideh Kouw, Wouter M. de Vries, Bert Neurons and Cognition Machine Learning Neural and Evolutionary Computing Bayesian inference provides a principled framework for understanding brain function, while neural activity in the brain is inherently spike-based. This paper bridges these two perspectives by designing spiking neural networks that simulate Bayesian inference through message passing for Bernoulli messages. To train the networks, we employ spike-timing-dependent plasticity, a biologically plausible mechanism for synaptic plasticity which is based on the Hebbian rule. Our results demonstrate that the network's performance closely matches the true numerical solution. We further demonstrate the versatility of our approach by implementing a factor graph example from coding theory, illustrating signal transmission over an unreliable channel. |
| title | Spike-Timing-Dependent Plasticity for Bernoulli Message Passing |
| topic | Neurons and Cognition Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2512.23728 |