This repository is the open source implementation of the Hierarchical Temporal Memory in C#/.NET Core. This repository contains set of libraries around NeoCortext API .NET Core library. NeoCortex API focuses implementation of Hierarchical Temporal Memory Cortical Learning Algorithm. Current version is first implementation of this algorithm on .NET platform. It includes the Spatial Pooler, Temporal Pooler, various encoders and CorticalNetwork algorithms. Implementation of this library aligns to existing Python and JAVA implementation of HTM. Due similarities between JAVA and C#, current API of SpatialPooler in C# is very similar to JAVA API. However the implementation of future versions will include some API changes to API style, which is additionally more aligned to C# community. This repository also cotains first experimental implementation of distributed highly scalable HTM CLA based on Actor Programming Model. The code published here is experimental code implemented during my research at daenet and Frankfurt University of Applied Sciences.
To get started, please see this document.
A Machine Learning Guide to HTM: https://numenta.com/blog/2019/10/24/machine-learning-guide-to-htm
Numenta on Github: https://github.com/numenta
HTM Community: https://numenta.org/
A deep dive in HTM Temporal Memory algorithm: https://numenta.com/assets/pdf/temporal-memory-algorithm/Temporal-Memory-Algorithm-Details.pdf
Continious Online Sequence Learning with HTM: https://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00893#.WMBBGBLytE6
Papers and conference proceedings
International Journal of Artificial Intelligence and Applications
Scaling the HTM Spatial Pooler
Dobric, Pech, Ghita, Wennekers 2020. 2020 International Journal of Artificial Intelligence and Applications. Scaling the HTM Spatial Pooler. doi:10.5121/ijaia .2020.11407
AIS 2020 - 6th International Conference on Artificial Intelligence and Soft Computing (AIS 2020), Helsinki
The Parallel HTM Spatial Pooler with Actor Model
Dobric, Pech, Ghita, Wennekers 2020. 2020 AIS 2020 - 6th International Conference on Artificial Intelligence and Soft Computing, Helsinki. The Parallel HTM Spatial Pooler with Actor Model. https://aircconline.com/csit/csit1006.pdf, doi:10.5121/csit.2020.100606
Symposium on Pattern Recognition and Applications - Rome, Italy
On the Relationship Between Input Sparsity and Noise Robustness in Hierarchical Temporal Memory Spatial Pooler
Dobric, Pech, Ghita, Wennekers 2020. 2020 Symposium on Pattern Recognition and Applications. On the Relationship Between Input Sparsity and Noise Robustness in Hierarchical Temporal Memory Spatial Pooler. https://dl.acm.org/doi/10.1145/3393822.3432317. doi:10.1145/3393822.3432317
International Conference on Pattern Recognition Applications and Methods - ICPRAM 2021
Improved HTM Spatial Pooler with Homeostatic Plasticity Control (Awarded with: Best Industrial Paper)
Dobric, Pech, Ghita, Wennekers 2021. ICPRAM Vienna Improved HTM Spatial Pooler with Homeostatic Plasticity control. doi:10.5220/0010314200980106
Springer Nature - Computer Sciences
On the Importance of the Newborn Stage When Learning Patterns with the Spatial Pooler
Dobric, Pech, Ghita, Wennekers 2022. Springer Nature Computer Science Journal On the Importance of the Newborn Stage When Learning Patterns with the Spatial Pooler. https://rdcu.be/cIcoc. doi:10.1007/s42979-022-01066-4
If your want to contribute on this project please contact us by opening an issue.