The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Spike processing with a graphene excitable laser
Open accessAbstract
Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.
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Cites
Simple model of spiking neurons
Izhikevich, E. M. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820440 · Cited by 4,828
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Synaptic plasticity: taming the beast
Abbott, L. F., & Nelson, S. B. (2000) · Nature Neuroscience · doi:10.1038/81453 · Cited by 2,263 · cited 1×
“Coincidence detection underlies a number of processing tasks, including associative memory 57 , and a form of temporal learning called spike timing-dependent plasticity (STDP) 58,59 .”
Analog Versus Digital: Extrapolating from Electronics to Neurobiology
Sarpeshkar, R. (1998) · Neural Computation · doi:10.1162/089976698300017052 · Cited by 496
Cited by
Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021) · Nature Photonics · doi:10.1038/s41566-020-00754-y · Cited by 1,756
Self-citationNeuromorphic photonic networks using silicon photonic weight banks
Tait, A. N., de Lima, T. F., Zhou, E., Wu, A. X., Nahmias, M. A., Shastri, B. J., & Prucnal, P. R. (2017) · Scientific Reports · doi:10.1038/s41598-017-07754-z · Cited by 849
Self-citationSilicon Photonic Modulator Neuron
Tait, A. N., Ferreira de Lima, T., Nahmias, M. A., Miller, H. B., Peng, H.-T., Shastri, B. J., & Prucnal, P. R. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.11.064043 · Cited by 331
Self-citationFeedback control for microring weight banks
Tait, A. N., Jayatilleka, H., De Lima, T. F., Ma, P. Y., Nahmias, M. A., Shastri, B. J., Shekhar, S., Chrostowski, L., & Prucnal, P. R. (2018) · Optics Express · doi:10.1364/oe.26.026422 · Cited by 138
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Metadata
- Authors
- Shastri, B. J., Nahmias, M. A., Tait, A. N., Rodriguez, A. W., Wu, B., & Prucnal, P. R.
- Venue
- Scientific Reports
- Published
- 2016-01-12
- Volume
- 6
- Issue
- 1
- Pages
- 19126-19126
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W3102053113
- Cited by
- 180
- References
- 4
- FWCI
- 20.47
- Type
- journal article
- Language
- en
- Open access
- gold
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