Related Experiment Video
Updated: Jan 27, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
Vector-Derived Transformation Binding: An Improved Binding Operation for Deep Symbol-Like Processing in Neural
Jan Gosmann1, Chris Eliasmith2
1Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L 3G1 Canada jgosmann@uwaterloo.ca.
Abstract:
We present a new binding operation, vector-derived transformation binding (VTB), for use in vector symbolic architectures (VSA). The performance of VTB is compared to circular convolution, used in holographic reduced representations (HRRs), in terms of list and stack encoding capacity. A special focus is given to the possibility of a neural implementation by the means of the Neural Engineering Framework (NEF). While the scaling of required neural resources is slightly worse for VTB, it is found to be on par with circular convolution for list encoding and better for encoding of stacks. Furthermore, VTB influences the vector length less, which also benefits a neural implementation. Consequently, we argue that VTB is an improvement over HRRs for neurally implemented VSAs.
Related Concept Videos
The Equilibrium Binding Constant and Binding Strength
The Equilibrium Binding Constant and Binding Strength
Nuclear Binding Energy
Ligand Binding Sites
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conserved Binding Sites
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
Conserved Binding Sites

