Authors and Acknowledgements

Primary Author

GNPower (Graham Power); powerg@mcmaster.ca

Contributors

See the full contributor list on GitHub: github.com/GNPower/PyHDC/graphs/contributors

To appear as a contributor, open a pull request following the guidelines in Contributing to PyHDC.

Academic foundations

The encoding families and operations in PyHDC are based on or inspired by the following papers:

  • Plate, T. A. (1995). Holographic reduced representations. IEEE Transactions on Neural Networks, 6(3), 623-641.

; Foundation of HRR, HRR_NoNorm, HRR_ConstNorm, FHRR encodings.

  • Kanerva, P. (2009). Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation, 1(2), 139-159.

; Foundation of MAP and BSC encodings; overview of HDC as a paradigm.

  • Rachkovskij, D. A., & Kussul, E. M. (2001). Binding and normalization of binary sparse distributed representations by context-dependent thinning. Neural Computation, 13(2), 411-452.

; Foundation of BSDC_CDT encoding.

  • Schlegel, K., Neubert, P., & Protzel, P. (2022). A comparison of vector symbolic architectures. Artificial Intelligence Review, 55(6), 4523-4555.

; Comparative survey of VSA families; basis for BSDC_THIN design.

  • Gosmann, J., & Eliasmith, C. (2019). Optimizing semantic pointer representations for symbol-like processing in spiking neural networks. PLOS ONE, 14(2), e0208128.

; Basis of VTB encoding.