18     References and sources

18.1   Own work

Malczan, A. (2012). Theory of the Neural Circuitry of the Brain and Analytical Thinking. Self-published, Oranienburg. ISBN 978-3-00-037458-6. Later published in full online at: https://www.andreas-malczan.de/teil-2-15.html (andreas-malczan.de in Bing)

Note: This monograph represents the first publication of the theory. The subsequent online version is a second publication, following the refusal by bookshops and Springer to accept it for economic reasons.

18.2   Historical Foundations of Attachment Theory

Singer, W. (1999). Neuronal Synchrony: A Versatile Code for the Definition of Relations? Neuron, 24(1), 49–65. (This is Singer’s seminal publication on the binding problem.)

Singer, W., & Gray, C. M. (1995). Visual Feature Integration and the Temporal Correlation Hypothesis. Annual Review of Neuroscience, 18, 555–586.

Singer, W. (2004). Time as Coding Space in Neocortical Processing. Current Opinion in Neurobiology, 14(2), 207–214.

von der Malsburg, C. (1981). The Correlation Theory of Brain Function. MPI Biophysical Chemistry, Internal Report 81-2. (Fundamental formulation of the correlation and binding theory.)

von der Malsburg, C. (1995). Binding in Models of Perception and Brain Function. Current Opinion in Neurobiology, 5(4), 520–526.

18.3   Neuroinformatics and Artificial Brains

Ramacher, U., & von der Malsburg, C. (2009). On the Construction of Artificial Brains. Springer, Berlin/Heidelberg.

This work combines neurobiological principles with technical architectures and is one of the most important German-language sources on systems-theoretical modelling of artificial brains.

18.4 Advanced Fundamentals: Learning Rules and Transformer Architecture

Oja, E. (1982). Simplified neuron model as a principal component analyzer. Journal of Mathematical Biology, 15(3), 267–273.
DOI: 10.1007/BF00275687

Sanger, T. D. (1989). Optimal unsupervised learning in a single‑layer linear feedforward neural network. Neural Networks, 2(6), 459–473.
DOI: 10.1016/0893-6080(89)90044-0

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need.
In: Advances in Neural Information Processing Systems (NeurIPS), 30.
Preprint: https://arxiv.org/abs/1706.03762

Note:

Parts of this manuscript were created using Microsoft Copilot and have been editorially revised. Copilot is an AI-powered assistance system based on modern recursive signal processing architectures that supports authors in structuring, formulating and linguistically optimising scientific texts. The theoretical content presented in this article is entirely the work of the author.