Theoretical Advances in Spiking Neural Networks with Hyperdimensional Computing

Karim Rahemtulla

Authors

Karim Rahemtulla KENYA Author

Keywords:

Spiking Neural Networks, Hyperdimensional Computing, theoretical advances, computational models

Abstract

Spiking Neural Networks (SNNs) have gained prominence in neuromorphic computing due to their biological plausibility and energy efficiency. Integrating Hyperdimensional Computing (HDC) with SNNs presents a promising avenue to enhance their computational capabilities. This paper explores theoretical advancements in leveraging HDC within SNN frameworks, focusing on encoding, computation, and learning paradigms. We review key theoretical insights, methodologies, and experimental validations, highlighting the potential of HDC to address complex cognitive tasks within the SNN architecture.

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