[
    {
        "id": "osp-16973",
        "type": "article-journal",
        "title": "Lock-in EP: An In-Situ Training Algorithm for Oscillatory Hardware",
        "author": [
            {
                "family": "Tammali",
                "given": "Sowjanya"
            },
            {
                "family": "Olin-Ammentorp",
                "given": "Wilkie"
            }
        ],
        "URL": "https://omanscience.com/en/articles/lock-in-ep-an-in-situ-training-algorithm-for-oscillatory-hardware",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Analog hardware platforms offer the potential to reduce energy consumption over digital architectures, but in order to succeed, large-scale analog systems must also be able to operate with or recover from the variability of their components. Towards this goal, we derive and demonstrate the lock-in equilibrium propagation (LIEP) training method. LIEP provides local gradient information for each component in an oscillatory network without separate forward and backward sweeps, potentially allowing for in-situ learning capabilities on analog oscillatory hardware platforms. We demonstrate that LIEP can be used both for ab-initio training as well as recovering performance when pre-trained parameters are perturbed. We show that LIEP can be formulated as a three-factor update rule, and suggest that although the method is currently only validated on shallow networks, alternate architectures may allow it to extend to deep and large-scale networks addressing complex tasks."
    }
]