Multimodal Physical Learning in Brain-Inspired Iontronic Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908633023905792 |
|---|---|
| author | Conte, Monica van Roij, René Dijkstra, Marjolein |
| author_facet | Conte, Monica van Roij, René Dijkstra, Marjolein |
| contents | Inspired by the brain, we present a physical alternative to traditional digital neural networks -- a microfluidic network in which nodes are connected by conical, electrolyte-filled channels acting as memristive iontronic synapses. Their electrical conductance responds not only to electrical signals, but also to chemical, mechanical, and geometric changes. Leveraging this multimodal responsiveness, we develop a training algorithm where learning is achieved by altering either the channel geometry or the applied stimuli. The network performs forward passes physically via ionic relaxation, while learning combines this physical evolution with numerical gradient descent. We theoretically demonstrate that this system can perform tasks like input-output mapping and linear regression with bias, paving the way for soft, adaptive materials that compute and learn without conventional electronics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Physical Learning in Brain-Inspired Iontronic Networks Conte, Monica van Roij, René Dijkstra, Marjolein Soft Condensed Matter Inspired by the brain, we present a physical alternative to traditional digital neural networks -- a microfluidic network in which nodes are connected by conical, electrolyte-filled channels acting as memristive iontronic synapses. Their electrical conductance responds not only to electrical signals, but also to chemical, mechanical, and geometric changes. Leveraging this multimodal responsiveness, we develop a training algorithm where learning is achieved by altering either the channel geometry or the applied stimuli. The network performs forward passes physically via ionic relaxation, while learning combines this physical evolution with numerical gradient descent. We theoretically demonstrate that this system can perform tasks like input-output mapping and linear regression with bias, paving the way for soft, adaptive materials that compute and learn without conventional electronics. |
| title | Multimodal Physical Learning in Brain-Inspired Iontronic Networks |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2511.04209 |