Bibliography

Academic papers and resources foundational to HoloVec.

Foundational Works

Kanerva, P. (1988). Sparse Distributed Memory. MIT Press. - Introduced sparse distributed memory concepts - Foundation for BSDC models

Plate, T. A. (1995). Holographic Reduced Representations: Distributed representation for cognitive structures. Artificial Intelligence, 75(1), 107-140. - Introduced HRR model - Circular convolution binding

Plate, T. A. (2003). Holographic Reduced Representations: Distributed Representation for Cognitive Structures. CSLI Publications. - Comprehensive treatment of HRR - Capacity analysis, applications

Gayler, R. W. (2003). Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience. Proceedings of ICCS/ASCS. - Coined "Vector Symbolic Architecture" - Relationship to cognitive science

Model-Specific Papers

FHRR

Plate, T. A. (2003). (See above) - FHRR as complex-valued HRR variant

MAP

Kanerva, P. (2009). Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors. Cognitive Computation, 1(2), 139-159. - Introduced MAP model - Tutorial introduction to HDC

BSC

Kanerva, P. (1996). Binary Spatter-Coding of Ordered K-Tuples. Proceedings of ICANN. - Introduced binary spatter codes - Sequence encoding

BSDC

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. - Sparse binary codes - Context-dependent binding

GHRR

Yeung, W., Zou, A., & Imani, F. (2024). Generalized Holographic Reduced Representations. arXiv:2405.09689. - Extended HRR to matrix domain - Non-commutative binding - State-of-the-art 2024

VTB

Gallant, S. I., & Okaywe, T. W. (2013). Representing Objects, Relations, and Sequences. Neural Computation, 25(8), 2038-2078. - Matrix Binding of Additive Terms (MBAT) - Non-commutative binding approach

Encoders

Fractional Power Encoding

Frady, E. P., Kleyko, D., & Sommer, F. T. (2021). Computing on Functions Using Randomized Vector Representations. arXiv:2109.03429. - VFA framework - Fractional power encoding theory - Kernel interpretation

Surveys and Comparisons

Schlegel, K., Neubert, P., & Protzel, P. (2022). A comparison of vector symbolic architectures. Artificial Intelligence Review, 55(4), 2583-2647. - Comprehensive model comparison - Capacity analysis - Benchmark results

Kleyko, D., Rachkovskij, D. A., Osipov, E., & Rahimi, A. (2023). A Survey on Hyperdimensional Computing: Theory, Algorithms, and Applications. ACM Computing Surveys, 55(6), 1-45. - Comprehensive HDC/VSA survey - Applications overview - Future directions

Cleanup and Retrieval

Kymn, C. J., Frady, E. P., & Sommer, F. T. (2024). Resonator Networks for Learning Compositional Representations. arXiv:2404.18789. - Resonator networks - Iterative factorization - Superior to brute force cleanup

Applications

Rahimi, A., et al. (2019). High-Dimensional Computing as a Nanoscalable Paradigm. IEEE Transactions on Circuits and Systems I, 66(11), 4266-4278. - Hardware implementation - Neuromorphic computing

Mitrokhin, A., Sutor, P., Fermüller, C., & Aloimonos, Y. (2019). Learning sensorimotor control with neuromorphic sensors: Toward hyperdimensional active perception. Science Robotics, 4(30). - Robotics application - Event-based sensing

Biological Foundations

Kanerva, P. (1993). Sparse distributed memory and related models. Associative Neural Memories, 50-76. - Biological plausibility - Connection to neural coding

Books

Kanerva, P. (1988). Sparse Distributed Memory. MIT Press. - Foundational text

Plate, T. A. (2003). Holographic Reduced Representations. CSLI Publications. - Complete HRR treatment

Eliasmith, C., & Anderson, C. H. (2003). Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems. MIT Press. - Neural perspective on VSA

Online Resources

HoloVec GitHub — This library
HDC/VSA Community — Research community
Penn Kanerva Group — Foundational research

Citation

If you use HoloVec in your research:

@software{HoloVec2025,
  author       = {Brodie Schroeder},
  title        = {HoloVec: Vector Symbolic Architectures for Python},
  year         = {2025},
  version      = {0.1.1},
  url          = {https://github.com/Twistient/HoloVec},
  license      = {Apache-2.0}
}