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Demonstrates enhanced learning performance in a neural network using homeostatic plasticity mechanisms.
Organic neuromorphic hardware has attracted significant interest because it readily offers excellent biocompatibility and supports biomimetic ionic signaling. However, emulating a combination of Hebbian plasticity with positive feedback and homeostatic plasticity for brain-like ensemble stabilization remains challenging at the multi-device level. To address this, we developed an ionic gating strategy employing a lateral global gate. The global gating triggers ion redistribution, enabling synchronous adjustment of the memristive strength across multiple polymer memristors. A small network of these memristors exhibits homeostatic global regulation while preserving functional differences among individual synaptic weights. Moreover, the simulations of an L1-constrained neural network with mapping of the device data demonstrate that an appropriate global weight budget is essential for optimal learning performance, indicating the superiority of incorporating a homeostatic stabilization mechanism in neuromorphic circuits.
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Wang et al. (2026) studied this question.
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