Yize Liu

All Publications

• Bio-plausible reconfigurable spiking neuron for neuromorphic computing. Science advances Xiao, Y., Liu, Y., Zhang, B., Chen, P., Zhu, H., He, E., Zhao, J., Huo, W., Jin, X., Zhang, X., Jiang, H., Ma, D., Zheng, Q., Tang, H., Lin, P., Kong, W., Pan, G. 2025; 11 (6): eadr6733

  Abstract

  Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behaviors due to high cost of emulating these biological spike patterns. Here, we propose a compact reconfigurable neuron design using the intrinsic dynamics of a NbO2-based spiking unit and excellent tunability in an electrochemical memory (ECRAM) to emulate the fast-slow dynamics in a bio-plausible neuron. The resistance of the ECRAM was effective in tuning the temporal dynamics of the membrane potential, contributing to flexible reconfiguration of various bio-plausible firing modes, such as phasic and burst spiking, and exhibiting adaptive spiking behaviors in changing environment. We used the bio-plausible neuron model to build spiking neural networks with bursting neurons and demonstrated improved classification accuracies over simplified models, showing great promises for use in more bio-plausible neuromorphic computing systems.

  View details for DOI 10.1126/sciadv.adr6733

  View details for PubMedID 39908388

  View details for PubMedCentralID PMC11797559

• Variation-resilient spike-timing-dependent plasticity in memristors using bursting neuron circuit NEUROMORPHIC COMPUTING AND ENGINEERING Liu, Y., Zhao, J., Xiao, Y., Chen, P., Chen, H., He, E., Lin, P., Pan, G. 2025; 5 (2)

  View details for DOI 10.1088/2634-4386/add9c0

  View details for Web of Science ID 001497335000001