MATLAB Simulation Codes for Photonic Memristor for Solar-Driven Neuromorphic Computing in Nonlinear Photonic Crystal Fibers
Fuente:
Zenodo
Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
|
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901076013219840 |
|---|---|
| author | Sharma, Mohit |
| author_facet | Sharma, Mohit |
| contents | <p>This repository contains the MATLAB simulation codes developed to support the theoretical investigation presented in the manuscript:</p> <blockquote> <p>M. Sharma, M. Goyal, Y. Sharma, M. T. A. Beig, <em>Photonic Memristor for Solar-Driven Neuromorphic Computing in Nonlinear Photonic Crystal Fibers</em> (submitted for publication).</p> </blockquote> <p>The codes implement the coupled generalized nonlinear Schrödinger equation (GNLSE) and rate‑equation model described in the paper, which captures the history‑dependent nonlinear response of AlGaAs‑doped glass photonic crystal fibers. Simulations cover the following functionalities:</p> <ul> <li> <p>Hysteresis characteristics and memristive behaviour (Figure 2)</p> </li> <li> <p>Spike‑timing‑dependent plasticity (STDP) and synaptic learning (Figure 3)</p> </li> <li> <p>Pattern recognition for 3‑bit temporal sequences (Figure 4)</p> </li> <li> <p>Reservoir computing with waveform classification (Figure 5)</p> </li> <li> <p>Chaotic time‑series prediction (Mackey‑Glass) with solar‑tunable memory (Figure 6)</p> </li> <li> <p>XOR neural network classification using a two‑layer photonic memristor network (Figure 7)</p> </li> </ul> <p>All simulations use the fiber parameters taken from the previously published work:<br><a href="https://doi.org/10.1109/JPHOT.2019.2927492" target="_blank" rel="noopener noreferrer">https://doi.org/10.1109/JPHOT.2019.2927492</a></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19140666 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | MATLAB Simulation Codes for Photonic Memristor for Solar-Driven Neuromorphic Computing in Nonlinear Photonic Crystal Fibers Sharma, Mohit <p>This repository contains the MATLAB simulation codes developed to support the theoretical investigation presented in the manuscript:</p> <blockquote> <p>M. Sharma, M. Goyal, Y. Sharma, M. T. A. Beig, <em>Photonic Memristor for Solar-Driven Neuromorphic Computing in Nonlinear Photonic Crystal Fibers</em> (submitted for publication).</p> </blockquote> <p>The codes implement the coupled generalized nonlinear Schrödinger equation (GNLSE) and rate‑equation model described in the paper, which captures the history‑dependent nonlinear response of AlGaAs‑doped glass photonic crystal fibers. Simulations cover the following functionalities:</p> <ul> <li> <p>Hysteresis characteristics and memristive behaviour (Figure 2)</p> </li> <li> <p>Spike‑timing‑dependent plasticity (STDP) and synaptic learning (Figure 3)</p> </li> <li> <p>Pattern recognition for 3‑bit temporal sequences (Figure 4)</p> </li> <li> <p>Reservoir computing with waveform classification (Figure 5)</p> </li> <li> <p>Chaotic time‑series prediction (Mackey‑Glass) with solar‑tunable memory (Figure 6)</p> </li> <li> <p>XOR neural network classification using a two‑layer photonic memristor network (Figure 7)</p> </li> </ul> <p>All simulations use the fiber parameters taken from the previously published work:<br><a href="https://doi.org/10.1109/JPHOT.2019.2927492" target="_blank" rel="noopener noreferrer">https://doi.org/10.1109/JPHOT.2019.2927492</a></p> |
| title | MATLAB Simulation Codes for Photonic Memristor for Solar-Driven Neuromorphic Computing in Nonlinear Photonic Crystal Fibers |
| url | https://doi.org/10.5281/zenodo.19140666 |