Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation
| Autor: | Simon Brown, Ford Wagner, Matthew D. Arnold, Philip J. Bones, Zachary E. Heywood, Joshua B. Mallinson, Edoardo Galli, S. K. Bose, Susant Kumar Acharya |
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| Rok vydání: | 2021 |
| Předmět: |
Neurons
Materials science Percolation (cognitive psychology) Quantitative Biology::Neurons and Cognition Random number generation Computation Information processing Brain Computer Science::Emerging Technologies Neuromorphic engineering Synapses General Materials Science Neural Networks Computer Nanoscience & Nanotechnology Neural coding Biological system 03 Chemical Sciences 09 Engineering |
| Zdroj: | ACS applied materialsinterfaces. 13(44) |
| ISSN: | 1944-8252 |
| Popis: | There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic spiking behavior that is strikingly similar to that observed in biological neurons. The spiking rate can be controlled by the input stimulus, similar to "rate coding" in biology, and the distributions of times between events are log-normal, providing insights into the atomic-scale spiking mechanism. The stochasticity of the spiking behavior is then used for true random number generation, and the high quality of the generated random bit-streams is demonstrated, opening up promising routes toward integration of neuromorphic computing with secure information processing. |
| Databáze: | OpenAIRE |
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