Contrastive Learning in Memristor-based Neuromorphic Systems
<|reference_start|>Contrastive Learning in Memristor-based Neuromorphic Systems: Spiking neural networks, the third generation of artificial neural networks, have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks, including their high energy inefficiency and long-criticized biological implausibility. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning. Our experimental simulations demonstrate that a hardware implementation of CSDP is capable of learning simple logic functions without the need to resort to complex gradient calculations.<|reference_end|>
@article{merkel2024contrastive,
    title={Contrastive Learning in Memristor-based Neuromorphic Systems},
    author={Cory Merkel, Alexander Ororbia},
    journal={arXiv preprint arXiv:2409.10887},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10887},
    primaryClass={cs.NE cs.ET q-bio.NC}
}