Authors and Acknowledgements ============================ Primary Author -------------- **GNPower** (Graham Power); `powerg@mcmaster.ca `_ * GitHub: `github.com/GNPower `_ * Project: `github.com/GNPower/PyHDC `_ 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 :doc:`../contributing/index`. 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.