https://github.com/candlelabai/stochastic-memristor-snn
Data of "Stochastic Memristive Devices for Low Cost Learning of Spatiotemporal Signals in Spiking Neural Networks" paper published in IOP Engineering Research Express 2025
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (5.1%) to scientific vocabulary
Repository
Data of "Stochastic Memristive Devices for Low Cost Learning of Spatiotemporal Signals in Spiking Neural Networks" paper published in IOP Engineering Research Express 2025
Basic Info
- Host: GitHub
- Owner: CandleLabAI
- Language: Python
- Default Branch: main
- Size: 70.3 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
A brief description of the data and code files:
"fig2.csv" : Simulated current-voltage response of RRAM device for 50 cycles.
"fig4.txt" : The values of conductances obtained after programming an RRAM device multilple times using five values of programming currents (25, 45, 65, 85,and 105 μA).
"fig6.txt" : A distribution of high resistance states achieved by cycling the device many times over.
"fig9.py" : A python script that shows the generation of random conductances by choosing different programming currents.
"fig1030meg.csv, fig1050meg.csv" : Simulated responses showing the evolution of membrane voltage for two values of the membrane time-constant setting memristor.
The corresponding python scripts can be run to replicate the figures from the manuscript.
Owner
- Login: CandleLabAI
- Kind: user
- Repositories: 2
- Profile: https://github.com/CandleLabAI
GitHub Events
Total
- Watch event: 1
- Push event: 1
- Create event: 4
Last Year
- Watch event: 1
- Push event: 1
- Create event: 4