Temporally Correlated Stochastic Spiking Neural Networks Enabled by 2D Ferroelectric Synapse‐Neuron Integration
Hanseo Jeon, Ui Do Ji, Sangmin Lee, Gihyeon Bhin, Chang Yong Park, Ji‐Sang Park, Byungchul Jang, Sungjoo Lee · 2026
Read Temporally Correlated Stochastic Spiking Neural Networks Enabled by 2D Ferroelectric Synapse‐Neuron Integration by Hanseo Jeon, Ui Do Ji, Sangmin Lee, Gihyeon Bhin, Chang Yong Park, Ji‐Sang Park, Byungchul Jang, Sungjoo Lee (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13669219.txt.
Description
Hanseo Jeon and co-authors build a chip neuron whose spikes keep a memory of past spikes in a 2026 paper. Brain-like computing needs chance in its spikes, for reasoning under doubt. Most chip neurons fire each spike fresh, with no tie to the last, unlike real neurons.
Their device pairs a ferroelectric transistor synapse with a layered crystal neuron. Spike odds are tuned by the synapse weight through voltage. Tests showed firing that rises and leaks like a real cell. Inner measures showed the neuron keeps a partial charge state, which links one spike to the next. A network of such units scored about ninety percent on a test task and held up under heavy noise.
Catalog record
| CREATOR | Hanseo Jeon, Ui Do Ji, Sangmin Lee, Gihyeon Bhin, Chang Yong Park, Ji‐Sang Park, Byungchul Jang, Sungjoo Lee |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |