Abstract
The neurobiological learning algorithm, spike-timing-dependent plasticity (STDP), is demonstrated in a simple photonic system using the cooperative nonlinear effects of cross gain modulation and nonlinear polarization rotation, and supervised and unsupervised learning using photonic neuron principles are examined. An STDP-based supervised learning scheme is presented which is capable of mimicking a desirable spike pattern through learning and adaptation. Furthermore, unsupervised learning is illustrated by a principal component analysis system operating under similar learning rules. Finally, a photonic-distributed processing network capable of STDP-based unsupervised learning is theoretically explored.
Original language | English (US) |
---|---|
Article number | 7234839 |
Pages (from-to) | 470-476 |
Number of pages | 7 |
Journal | Journal of Lightwave Technology |
Volume | 34 |
Issue number | 2 |
DOIs | |
State | Published - Jan 15 2016 |
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
Keywords
- Feedback circuits
- information theory
- neural networks
- nonlinear optics
- optical signal processing