Behavioral Time Scale Synaptic Plasticity (BTSP) endows Hyperdimensional Computing with brain-like information retrieval flexibility

Hyperdimensional computing (HDC) addresses massively parallel implementations of symbolic computations that are both more transparent than ANNs and LLMs and more suitable for in-memory computing on highly energy-efficient analog hardware. It captures an essential aspects of brain computations: objects, concepts, and their attributes are encoded by very sparse distributed representations. But currently known methods for binding these tokens together entail deficits in flexible information retrieval. We show that a mechanism which the brain employs for binding, Behavioral Time Scale Synaptic Plasticity (BTSP), overcomes these deficiencies by adding attractor features to high-dimensional representations. They drastically improve the capability to recover from composed representations the tokens which have been bound together in them. One arrives in this way at a functionally more powerful HDC paradigm that provides new perspectives both for understanding how brains carry out symbolic computations, and for implementing them in novel energy-efficient and massively parallel neuromorphic hardware.